{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T21:42:59Z","timestamp":1781818979527,"version":"3.54.5"},"reference-count":480,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper provides a systematic survey of artificial intelligence (AI) models that have been proposed over the past decade to screen retinal diseases, which can cause severe visual impairments or even blindness. The paper covers both the clinical and technical perspectives of using AI models in hosipitals to aid ophthalmologists in promptly identifying retinal diseases in their early stages. Moreover, this paper also evaluates various methods for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of the existing literature. This comprehensive study, which reviews both the conventional and state-of-the-art methods to screen retinopathy across different modalities, is unique in its scope. Additionally, this paper serves as a helpful guide for researchers who want to work in the field of retinal image analysis in the future.<\/jats:p>","DOI":"10.1007\/s10462-024-10736-z","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T05:01:46Z","timestamp":1712293306000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A comprehensive review of artificial intelligence models for screening major retinal diseases"],"prefix":"10.1007","volume":"57","author":[{"given":"Bilal","family":"Hassan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hina","family":"Raja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taimur","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Usman","family":"Akram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hira","family":"Raja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alaa A.","family":"Abd-alrazaq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siamak","family":"Yousefi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoufel","family":"Werghi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"key":"10736_CR1","unstructured":"A Crowd-Sourcing Platform for Labeling Fundus Images (2019). https:\/\/www.labelme.org\/. Accessed 25 Nov 2022"},{"issue":"1","key":"10736_CR2","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1002\/ima.22621","volume":"32","author":"L Abdel-Hamid","year":"2022","unstructured":"Abdel-Hamid L (2022) TWEEC: computer-aided glaucoma diagnosis from retinal images using deep learning techniques. Int J Imaging Syst Technol 32(1):387\u2013401","journal-title":"Int J Imaging Syst Technol"},{"key":"10736_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/1698967","volume":"2019","author":"MK Abdellatif","year":"2019","unstructured":"Abdellatif MK, Elzankalony YAM, Ebeid AAA, Ebeid WM (2019) Outer retinal layers\u2019 thickness changes in relation to age and choroidal thickness in normal eyes. J Ophthalmol 2019:1\u20138. https:\/\/doi.org\/10.1155\/2019\/1698967","journal-title":"J Ophthalmol"},{"key":"10736_CR4","doi-asserted-by":"publisher","first-page":"37311","DOI":"10.1109\/ACCESS.2021.3061451","volume":"9","author":"F Abdullah","year":"2021","unstructured":"Abdullah F, Imtiaz R, Madni HA, Khan HA, Khan TM, Khan MA, Naqvi SS (2021) A review on glaucoma disease detection using computerized techniques. IEEE Access 9:37311\u201337333","journal-title":"IEEE Access"},{"key":"10736_CR5","doi-asserted-by":"crossref","unstructured":"Abhishek AM, Berendschot TT, Rao SV, Dabir S (2014) Segmentation and analysis of retinal layers (ILM & RPE) in optical coherence tomography images with edema. In: 2014 IEEE conference on biomedical engineering and sciences (IECBES), IEEE, pp 204\u2013209","DOI":"10.1109\/IECBES.2014.7047486"},{"issue":"2","key":"10736_CR6","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1111\/ceo.14035","volume":"50","author":"IF Aboobakar","year":"2022","unstructured":"Aboobakar IF, Wiggs JL (2022) The genetics of glaucoma: disease associations, personalised risk assessment and therapeutic opportunities\u2014a review. Clin Exp Ophthalmol 50(2):143\u2013162","journal-title":"Clin Exp Ophthalmol"},{"key":"10736_CR8","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","volume":"3","author":"MD Abramoff","year":"2010","unstructured":"Abramoff MD, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Rev Biomed Eng 3:169\u2013208. https:\/\/doi.org\/10.1109\/RBME.2010.2084567","journal-title":"IEEE Rev Biomed Eng"},{"key":"10736_CR9","unstructured":"Adak C, Karkera T, Chattopadhyay S, Saqib M (2023) Detecting severity of diabetic retinopathy from fundus images using ensembled transformers. Preprint arXiv:2301.00973"},{"issue":"5","key":"10736_CR10","doi-asserted-by":"publisher","first-page":"051403","DOI":"10.1117\/1.2793736","volume":"12","author":"Z Adam","year":"2007","unstructured":"Adam Z, Freddy N, LOldenburg O, Marks ML, Boppart BA (2007) Optical coherence tomography: a review of clinical development from bench to bedside. J Biomed Opt 12(5):051403. https:\/\/doi.org\/10.1117\/1.2793736","journal-title":"J Biomed Opt"},{"issue":"11","key":"10736_CR11","doi-asserted-by":"publisher","first-page":"e0207982","DOI":"10.1371\/journal.pone.0207982","volume":"13","author":"JM Ahn","year":"2018","unstructured":"Ahn JM, Kim S, Ahn KS, Cho SH, Lee KB, Kim US (2018) A deep learning model for the detection of both advanced and early glaucoma using fundus photography. PLoS One 13(11):e0207982","journal-title":"PLoS One"},{"issue":"1","key":"10736_CR12","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.patcog.2012.07.002","volume":"46","author":"MU Akram","year":"2013","unstructured":"Akram MU, Khalid S, Khan SA (2013) Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recogn 46(1):107\u2013116","journal-title":"Pattern Recogn"},{"key":"10736_CR13","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.compbiomed.2013.11.014","volume":"45","author":"MU Akram","year":"2014","unstructured":"Akram MU, Khalid S, Tariq A, Khan SA, Azam F (2014) Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med 45:161\u2013171","journal-title":"Comput Biol Med"},{"issue":"2","key":"10736_CR14","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.cmpb.2014.01.010","volume":"114","author":"MU Akram","year":"2014","unstructured":"Akram MU, Tariq A, Khan SA, Javed MY (2014) Automated detection of exudates and macula for grading of diabetic macular edema. Comput Methods Programs Biomed 114(2):141\u2013152","journal-title":"Comput Methods Programs Biomed"},{"issue":"4","key":"10736_CR15","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1007\/s13246-015-0377-y","volume":"38","author":"MU Akram","year":"2015","unstructured":"Akram MU, Tariq A, Khalid S, Javed MY, Abbas S, Yasin UU (2015) Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques. Aus Phys Eng Sci Med 38(4):643\u2013655","journal-title":"Aus Phys Eng Sci Med"},{"issue":"9","key":"10736_CR16","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.jfo.2019.12.020","volume":"43","author":"F Aksoy","year":"2020","unstructured":"Aksoy F, Altan C, Y\u0131lmaz B, Y\u0131lmaz I, Tun\u00e7 U, Kesim C, Kocamaz M, Pasaoglu I (2020) A comparative evaluation of segmental analysis of macular layers in patients with early glaucoma, ocular hypertension, and healthy eyes. J Fr Ophtalmol 43(9):869\u2013878","journal-title":"J Fr Ophtalmol"},{"issue":"12","key":"10736_CR17","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.3390\/polym14122359","volume":"14","author":"KA Akulo","year":"2022","unstructured":"Akulo KA, Adali T, Moyo MTG, Bodamyali T (2022) Intravitreal injectable hydrogels for sustained drug delivery in glaucoma treatment and therapy. Polymers 14(12):2359","journal-title":"Polymers"},{"issue":"174","key":"10736_CR18","first-page":"166","volume":"904","author":"A Al Mamun","year":"2021","unstructured":"Al Mamun A, Mimi AA, Zaeem M, Wu Y, Monalisa I, Akter A, Munir F, Xiao J (2021) Role of pyroptosis in diabetic retinopathy and its therapeutic implications. Eur J Pharmacol 904(174):166","journal-title":"Eur J Pharmacol"},{"key":"10736_CR19","doi-asserted-by":"crossref","unstructured":"Alam MN, Yamashita R, Ramesh V, Prabhune T, Lim JI, Chan RVP, Hallak J, Leng T, Rubin D (2022) Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models. Preprint arXiv:2208.11563","DOI":"10.21203\/rs.3.rs-2199633\/v1"},{"issue":"4","key":"10736_CR20","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3390\/sym10040087","volume":"10","author":"B Al-Bander","year":"2018","unstructured":"Al-Bander B, Williams BM, Al-Nuaimy W, Al-Taee MA, Pratt H, Zheng Y (2018) Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis. Symmetry 10(4):87","journal-title":"Symmetry"},{"key":"10736_CR21","doi-asserted-by":"crossref","unstructured":"Alhasson HF, Alharbi SS, Obara B (2018) 2d and 3d vascular structures enhancement via multiscale fractional anisotropy tensor. In: Proceedings of the European conference on computer vision (ECCV) workshops","DOI":"10.1007\/978-3-030-11024-6_26"},{"key":"10736_CR22","doi-asserted-by":"publisher","first-page":"165056","DOI":"10.1109\/ACCESS.2020.3022943","volume":"8","author":"M Alhussein","year":"2020","unstructured":"Alhussein M, Aurangzeb K, Haider SI (2020) An unsupervised retinal vessel segmentation using hessian and intensity based approach. IEEE Access 8:165056\u2013165070","journal-title":"IEEE Access"},{"key":"10736_CR23","doi-asserted-by":"crossref","unstructured":"Ali A, Hussain A, Zaki WMDW (2017) Vessel extraction in retinal images using automatic thresholding and Gabor wavelet. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 365\u2013368","DOI":"10.1109\/EMBC.2017.8036838"},{"key":"10736_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/4061313","volume":"2019","author":"G An","year":"2019","unstructured":"An G, Omodaka K, Hashimoto K, Tsuda S, Shiga Y, Takada N, Kikawa T, Yokota H, Akiba M, Nakazawa T (2019) Glaucoma diagnosis with machine learning based on optical coherence tomography and color fundus images. J Healthc Eng 2019:1\u20139. https:\/\/doi.org\/10.1155\/2019\/4061313","journal-title":"J Healthc Eng"},{"issue":"1","key":"10736_CR26","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.survophthal.2021.01.015","volume":"67","author":"JCF Andrade","year":"2022","unstructured":"Andrade JCF, Kanadani FN, Furlanetto RL, Lopes FS, Ritch R, Prata TS (2022) Elucidation of the role of the lamina cribrosa in glaucoma using optical coherence tomography. Surv Ophthalmol 67(1):197\u2013216","journal-title":"Surv Ophthalmol"},{"issue":"2","key":"10736_CR27","doi-asserted-by":"publisher","first-page":"e0213110","DOI":"10.1371\/journal.pone.0213110","volume":"14","author":"T Araki","year":"2019","unstructured":"Araki T, Ishikawa H, Iwahashi C, Niki M, Mitamura Y, Sugimoto M, Kondo M, Kinoshita T, Nishi T, Ueda T et al (2019) Central serous chorioretinopathy with and without steroids: a multicenter survey. PloS One 14(2):e0213110","journal-title":"PloS One"},{"issue":"6","key":"10736_CR28","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.3390\/genes13061055","volume":"13","author":"NG Asefa","year":"2022","unstructured":"Asefa NG, Kamali Z, Pereira S, Vaez A, Jansonius N, Bergen AA, Snieder H (2022) Bioinformatic prioritization and functional annotation of GWAS-based candidate genes for primary open-angle glaucoma. Genes 13(6):1055","journal-title":"Genes"},{"key":"10736_CR29","doi-asserted-by":"crossref","unstructured":"Asgari R, Orlando JI, Waldstein S, Schlanitz F, Baratsits M, Schmidt-Erfurth U, Bogunovi\u0107 H (2019) Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 192\u2013200","DOI":"10.1007\/978-3-030-32239-7_22"},{"key":"10736_CR30","doi-asserted-by":"publisher","first-page":"2250033","DOI":"10.4015\/S1016237222500338","volume":"34","author":"Kaur M Ashanand","year":"2022","unstructured":"Ashanand Kaur M (2022) Efficient retinal image enhancement using morphological operations. Biomed Eng Appl Basis Commun 34:2250033","journal-title":"Biomed Eng Appl Basis Commun"},{"key":"10736_CR31","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.ajo.2021.03.030","volume":"228","author":"J Aspberg","year":"2021","unstructured":"Aspberg J, Heijl A, Bengtsson B (2021) Screening for open-angle glaucoma and its effect on blindness. Am J Ophthalmol 228:106\u2013116","journal-title":"Am J Ophthalmol"},{"issue":"4","key":"10736_CR32","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1007\/s12325-020-01277-2","volume":"37","author":"S Asrani","year":"2020","unstructured":"Asrani S, Bacharach J, Holland E, McKee H, Sheng H, Lewis RA, Kopczynski CC, Heah T (2020) Fixed-dose combination of netarsudil and latanoprost in ocular hypertension and open-angle glaucoma: pooled efficacy\/safety analysis of phase 3 mercury-1 and-2. Adv Ther 37(4):1620\u20131631","journal-title":"Adv Ther"},{"issue":"8","key":"10736_CR33","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1016\/j.patrec.2012.11.002","volume":"34","author":"G Azzopardi","year":"2013","unstructured":"Azzopardi G, Petkov N (2013) Automatic detection of vascular bifurcations in segmented retinal images using trainable cosfire filters. Pattern Recogn Lett 34(8):922\u2013933","journal-title":"Pattern Recogn Lett"},{"issue":"20","key":"10736_CR34","doi-asserted-by":"publisher","first-page":"2128","DOI":"10.3923\/jas.2012.2128.2138","volume":"12","author":"TG Babu","year":"2012","unstructured":"Babu TG, Devi SS, Venkatesh R (2012) Automatic detection of glaucoma using optical coherence tomography image. J Appl Sci 12(20):2128\u20132138. https:\/\/doi.org\/10.3923\/jas.2012.2128.2138","journal-title":"J Appl Sci"},{"issue":"100","key":"10736_CR35","first-page":"203","volume":"35","author":"M Badar","year":"2020","unstructured":"Badar M, Haris M, Fatima A (2020) Application of deep learning for retinal image analysis: a review. Comput Sci Rev 35(100):203","journal-title":"Comput Sci Rev"},{"issue":"4","key":"10736_CR36","first-page":"603","volume":"5","author":"A Baghaie","year":"2015","unstructured":"Baghaie A, Yu Z, D\u2019Souza RM (2015) State-of-the-art in retinal optical coherence tomography image analysis. Quant Imaging Med Surg 5(4):603","journal-title":"Quant Imaging Med Surg"},{"key":"10736_CR37","doi-asserted-by":"crossref","unstructured":"Bajwa MN, Singh GAP, Neumeier W, Malik MI, Dengel A, Ahmed S (2020) G1020: a benchmark retinal fundus image dataset for computer-aided glaucoma detection. In: 2020 international joint conference on neural networks (IJCNN). IEEE, pp 1\u20137","DOI":"10.1109\/IJCNN48605.2020.9207664"},{"issue":"7","key":"10736_CR38","doi-asserted-by":"publisher","first-page":"071010","DOI":"10.1149\/2162-8777\/ac0e49","volume":"10","author":"A Bala","year":"2021","unstructured":"Bala A, Maik V et al (2021) Contrast and luminance enhancement technique for fundus images using bi-orthogonal wavelet transform and bilateral filter. ECS J Solid State Sci Technol 10(7):071010","journal-title":"ECS J Solid State Sci Technol"},{"key":"10736_CR39","first-page":"2723","volume":"24","author":"MP Bala","year":"2021","unstructured":"Bala MP, Rajalakshmi P, Sindhuja AM, Naganandhini S (2021) A review on recent development for diagnosis of glaucoma. Ann Rom Soc Cell Biol 24:2723\u20132736","journal-title":"Ann Rom Soc Cell Biol"},{"key":"10736_CR40","unstructured":"Bastelica P, Labb\u00e9 A, El Maftouhi A, Hamard P, Paques M, Baudouin C (2022) Role of the lamina cribrosa in the pathogenesis of glaucoma: a review of the literature. J Francais D\u2019ophtalmol pp S0181\u20135512"},{"issue":"4","key":"10736_CR41","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.ophtha.2021.11.014","volume":"129","author":"Bhandari S, Vitale S, Agr\u00f3n E, Clemons TE, Chew EY, Study ARED, Group R","year":"2022","unstructured":"Bhandari S, Vitale S, Agr\u00f3n E, Clemons TE, Chew EY, Study ARED, Group R (2022) Cataract surgery and the risk of developing late age-related macular degeneration: the age-related eye disease study 2 report number 27. Ophthalmology 129(4):414\u2013420","journal-title":"Ophthalmology"},{"key":"10736_CR42","doi-asserted-by":"crossref","unstructured":"Bhimavarapu U, Battineni G (2023) Deep learning for the detection and classification of diabetic retinopathy with an improved activation function. In: Healthcare, Multidisciplinary Digital Publishing Institute, vol 11, p 97","DOI":"10.3390\/healthcare11010097"},{"issue":"106","key":"10736_CR45","first-page":"165","volume":"90","author":"TRV Bisneto","year":"2020","unstructured":"Bisneto TRV, de Carvalho Filho AO, Magalh\u00e3es DMV (2020) Generative adversarial network and texture features applied to automatic glaucoma detection. Appl Soft Comput 90(106):165","journal-title":"Appl Soft Comput"},{"issue":"1","key":"10736_CR46","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3390\/biomedicines10010088","volume":"10","author":"A Boned-Murillo","year":"2022","unstructured":"Boned-Murillo A, Albertos-Arranz H, Diaz-Barreda MD, S\u00e1nchez-Cano A, Ferreras A, Cuenca N, Pinilla I et al (2022) Optical coherence tomography angiography in diabetic patients: a systematic review. Biomedicines 10(1):88","journal-title":"Biomedicines"},{"issue":"7","key":"10736_CR47","doi-asserted-by":"publisher","first-page":"3968","DOI":"10.1364\/BOE.395279","volume":"11","author":"S Borkovkina","year":"2020","unstructured":"Borkovkina S, Camino A, Janpongsri W, Sarunic MV, Jian Y (2020) Real-time retinal layer segmentation of oct volumes with GPU accelerated inferencing using a compressed, low-latency neural network. Biomed Opt Express 11(7):3968\u20133984","journal-title":"Biomed Opt Express"},{"issue":"101","key":"10736_CR48","first-page":"902","volume":"90","author":"H Boudegga","year":"2021","unstructured":"Boudegga H, Elloumi Y, Akil M, Bedoui MH, Kachouri R, Abdallah AB (2021) Fast and efficient retinal blood vessel segmentation method based on deep learning network. Comput Med Imaging Graph 90(101):902","journal-title":"Comput Med Imaging Graph"},{"key":"10736_CR49","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.ajo.2016.11.010","volume":"175","author":"C Bowd","year":"2017","unstructured":"Bowd C, Zangwill LM, Weinreb RN, Medeiros FA, Belghith A (2017) Estimating optical coherence tomography structural measurement floors to improve detection of progression in advanced glaucoma. Am J Ophthalmol 175:37\u201344","journal-title":"Am J Ophthalmol"},{"issue":"5","key":"10736_CR51","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.ogla.2020.05.008","volume":"3","author":"JW Brubaker","year":"2020","unstructured":"Brubaker JW, Teymoorian S, Lewis RA, Usner D, McKee HJ, Ramirez N, Kopczynski CC, Heah T (2020) One year of netarsudil and latanoprost fixed-dose combination for elevated intraocular pressure: phase 3, randomized mercury-1 study. Ophthalmol Glaucoma 3(5):327\u2013338","journal-title":"Ophthalmol Glaucoma"},{"key":"10736_CR52","doi-asserted-by":"publisher","first-page":"109099","DOI":"10.1016\/j.exer.2022.109099","volume":"220","author":"Y Bu","year":"2022","unstructured":"Bu Y, Shih KC, Tong L (2022) The ocular surface and diabetes, the other 21st century epidemic. Exp Eye Res 220:109099","journal-title":"Exp Eye Res"},{"issue":"109","key":"10736_CR53","first-page":"426","volume":"134","author":"\u00dc Budak","year":"2020","unstructured":"Budak \u00dc, C\u00f6mert Z, \u00c7\u0131buk M, \u015eeng\u00fcr A (2020) DCCMED-Net: densely connected and concatenated multi encoder-decoder CNNs for retinal vessel extraction from fundus images. Med Hypotheses 134(109):426","journal-title":"Med Hypotheses"},{"issue":"9","key":"10736_CR54","doi-asserted-by":"publisher","first-page":"5017","DOI":"10.1364\/BOE.395487","volume":"11","author":"A Butola","year":"2020","unstructured":"Butola A, Prasad DK, Ahmad A, Dubey V, Qaiser D, Srivastava A, Senthilkumaran P, Ahluwalia BS, Mehta DS (2020) Deep learning architecture LightOCT for diagnostic decision support using optical coherence tomography images of biological samples. Biomed Opt Express 11(9):5017\u20135031","journal-title":"Biomed Opt Express"},{"issue":"7","key":"10736_CR55","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.3390\/diagnostics12071607","volume":"12","author":"MM Butt","year":"2022","unstructured":"Butt MM, Iskandar DA, Abdelhamid SE, Latif G, Alghazo R (2022) Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features. Diagnostics 12(7):1607","journal-title":"Diagnostics"},{"issue":"10","key":"10736_CR57","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1167\/iovs.62.10.34","volume":"62","author":"D Cao","year":"2021","unstructured":"Cao D, Leong B, Messinger JD, Kar D, Ach T, Yannuzzi LA, Freund KB, Curcio CA (2021) Hyperreflective foci, optical coherence tomography progression indicators in age-related macular degeneration, include transdifferentiated retinal pigment epithelium. Investig Ophthalmol Vis Sci 62(10):34","journal-title":"Investig Ophthalmol Vis Sci"},{"issue":"105","key":"10736_CR58","first-page":"341","volume":"144","author":"P Cao","year":"2022","unstructured":"Cao P, Hou Q, Song R, Wang H, Zaiane O (2022) Collaborative learning of weakly-supervised domain adaptation for diabetic retinopathy grading on retinal images. Comput Biol Med 144(105):341","journal-title":"Comput Biol Med"},{"issue":"7","key":"10736_CR59","doi-asserted-by":"publisher","first-page":"e220122","DOI":"10.1530\/EC-22-0122","volume":"11","author":"X Cao","year":"2022","unstructured":"Cao X, Lu M, Xie RR, Song LN, Yang WL, Xin Z, Yang GR, Yang JK (2022) A high TSH level is associated with diabetic macular edema: a cross-sectional study of patients with type 2 diabetes mellitus. Endocr Connect 11(7):e220122","journal-title":"Endocr Connect"},{"key":"10736_CR60","doi-asserted-by":"publisher","first-page":"85905","DOI":"10.1109\/ACCESS.2022.3198657","volume":"10","author":"A Caza\u00f1as-Gord\u00f3n","year":"2022","unstructured":"Caza\u00f1as-Gord\u00f3n A, da Silva Cruz LA (2022) Multiscale attention gated network (magnet) for retinal layer and macular cystoid edema segmentation. IEEE Access 10:85905\u201385917","journal-title":"IEEE Access"},{"issue":"12","key":"10736_CR61","doi-asserted-by":"publisher","first-page":"1950","DOI":"10.3390\/cells11121950","volume":"11","author":"MZ Chauhan","year":"2022","unstructured":"Chauhan MZ, Rather PA, Samarah SM, Elhusseiny AM, Sallam AB (2022) Current and novel therapeutic approaches for treatment of diabetic macular edema. Cells 11(12):1950","journal-title":"Cells"},{"key":"10736_CR62","first-page":"254","volume":"107","author":"TC Chen","year":"2009","unstructured":"Chen TC (2009) Spectral domain optical coherence tomography in glaucoma: qualitative and quantitative analysis of the optic nerve head and retinal nerve fiber layer (an AOS thesis). Trans Am Ophthalmol Soc 107:254","journal-title":"Trans Am Ophthalmol Soc"},{"key":"10736_CR63","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1303-8","author":"Z Chen","year":"2019","unstructured":"Chen Z, Zheng X, Shen H, Zeng Z, Liu Q, Li Z (2019) Combination of enhanced depth imaging optical coherence tomography and fundus images for glaucoma screening. J Med Syst. https:\/\/doi.org\/10.1007\/s10916-019-1303-8","journal-title":"J Med Syst"},{"issue":"1","key":"10736_CR64","doi-asserted-by":"publisher","first-page":"e0262689","DOI":"10.1371\/journal.pone.0262689","volume":"17","author":"D Chen","year":"2022","unstructured":"Chen D, Yang W, Wang L, Tan S, Lin J, Bu W (2022) PCAT-UNet: UNet-like network fused convolution and transformer for retinal vessel segmentation. PLoS One 17(1):e0262689","journal-title":"PLoS One"},{"key":"10736_CR65","doi-asserted-by":"crossref","unstructured":"Chen H, Wang M, Xia L, Dong J, Xu G, Wang Z, Feng L, Zhou Y (2022b) New evidence of central nervous system damage in diabetes mellitus: Impairment of fine visual discrimination. Diabetes","DOI":"10.2337\/figshare.19807627"},{"issue":"2","key":"10736_CR66","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1002\/jcb.30195","volume":"123","author":"J Chen","year":"2022","unstructured":"Chen J, Luo SF, Yuan X, Wang M, Yu HJ, Zhang Z, Yang YY (2022) Diabetic kidney disease-predisposing proinflammatory and profibrotic genes identified by weighted gene co-expression network analysis (WGCNA). J Cell Biochem 123(2):481\u2013492","journal-title":"J Cell Biochem"},{"key":"10736_CR67","doi-asserted-by":"crossref","unstructured":"Chen S, Ma D, Lee S, Yu TT, Xu G, Lu D, Popuri K, Ju MJ, Sarunic MV, Beg MF (2022d) Segmentation-guided domain adaptation and data harmonization of multi-device retinal optical coherence tomography using cycle-consistent generative adversarial networks. Preprint arXiv:2208.14635","DOI":"10.1016\/j.compbiomed.2023.106595"},{"issue":"7","key":"10736_CR68","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1007\/s11606-016-3604-7","volume":"31","author":"MH Chin","year":"2016","unstructured":"Chin MH (2016) Creating the business case for achieving health equity. J Gen Intern Med 31(7):792\u2013796","journal-title":"J Gen Intern Med"},{"issue":"4","key":"10736_CR69","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1364\/BOE.6.001172","volume":"6","author":"SJ Chiu","year":"2015","unstructured":"Chiu SJ, Allingham MJ, Mettu PS, Cousins SW, Izatt JA, Farsiu S (2015) Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. Biomed Opt Express 6(4):1172\u20131194","journal-title":"Biomed Opt Express"},{"issue":"4","key":"10736_CR70","doi-asserted-by":"publisher","first-page":"678","DOI":"10.3390\/pr10040678","volume":"10","author":"YK Cho","year":"2022","unstructured":"Cho YK, Lee SM, Kang YJ, Kang YM, Jeon IC, Park DH (2022) The age-related macular degeneration (AMD)-preventing mechanism of natural products. Processes 10(4):678","journal-title":"Processes"},{"key":"10736_CR71","doi-asserted-by":"crossref","unstructured":"Choquet H, Khawaja AP, Jiang C, Yin J, Melles RB, Glymour MM, Hysi PG, Jorgenson E (2022) Association between myopic refractive error and primary open-angle glaucoma: a 2-sample Mendelian randomization study. JAMA Ophthalmol","DOI":"10.1001\/jamaophthalmol.2022.2762"},{"issue":"20","key":"10736_CR72","doi-asserted-by":"publisher","first-page":"1998","DOI":"10.1001\/jama.2022.6290","volume":"327","author":"R Chou","year":"2022","unstructured":"Chou R, Selph S, Blazina I, Bougatsos C, Jungbauer R, Fu R, Grusing S, Jonas DE, Tehrani S (2022) Screening for glaucoma in adults: updated evidence report and systematic review for the us preventive services task force. JAMA 327(20):1998\u20132012","journal-title":"JAMA"},{"issue":"5","key":"10736_CR73","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1136\/bjophthalmol-2020-316218","volume":"106","author":"SY Chua","year":"2022","unstructured":"Chua SY, Warwick A, Peto T, Balaskas K, Moore AT, Reisman C, Desai P, Lotery AJ, Dhillon B, Khaw PT et al (2022) Association of ambient air pollution with age-related macular degeneration and retinal thickness in UK biobank. Br J Ophthalmol 106(5):705\u2013711","journal-title":"Br J Ophthalmol"},{"issue":"3","key":"10736_CR74","doi-asserted-by":"publisher","first-page":"484","DOI":"10.3390\/jcm10030484","volume":"10","author":"AC Cohn","year":"2021","unstructured":"Cohn AC, Wu Z, Jobling AI, Fletcher EL, Guymer RH (2021) Subthreshold nano-second laser treatment and age-related macular degeneration. J Clin Med 10(3):484","journal-title":"J Clin Med"},{"key":"10736_CR75","doi-asserted-by":"crossref","unstructured":"Crick RP, Khaw PT (2003) Textbook of clinical ophthalmology, A: a practical guide to disorders of the eyes and their management. World Scientific Publishing Company","DOI":"10.1142\/5074"},{"key":"10736_CR76","doi-asserted-by":"publisher","first-page":"109134","DOI":"10.1016\/j.exer.2022.109134","volume":"221","author":"K Csaky","year":"2022","unstructured":"Csaky K, Curcio CA, Mullins RF, Rosenfeld PJ, Fujimoto J, Rohrer B, Ribero R, Malek G, Waheed N, Guymer R et al (2022) New approaches to the treatment of age-related macular degeneration (AMD). Exp Eye Res 221:109134","journal-title":"Exp Eye Res"},{"issue":"4","key":"10736_CR77","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1111\/bph.15683","volume":"179","author":"QN Cui","year":"2022","unstructured":"Cui QN, Stein LM, Fortin SM, Hayes MR (2022) The role of glia in the physiology and pharmacology of glucagon-like peptide-1: implications for obesity, diabetes, neurodegeneration and glaucoma. Br J Pharmacol 179(4):715\u2013726","journal-title":"Br J Pharmacol"},{"issue":"8","key":"10736_CR78","first-page":"1656","volume":"62","author":"C Czerpak","year":"2021","unstructured":"Czerpak C, Ling YTT, Jefferys JL, Quigley HA, Nguyen TD (2021) The curvature and collagen network of the human lamina cribrosa in glaucoma and control eyes. Investig Ophthalmol Vis Sci 62(8):1656","journal-title":"Investig Ophthalmol Vis Sci"},{"key":"10736_CR79","doi-asserted-by":"crossref","unstructured":"Czerpak CA, Kashaf MS, Zimmerman BK, Quigley HA, Nguyen TD (2022) The strain response to intraocular pressure decrease in the lamina cribrosa of glaucoma patients. Ophthalmol Glaucoma","DOI":"10.1016\/j.ogla.2022.07.005"},{"key":"10736_CR80","doi-asserted-by":"crossref","unstructured":"Das S, Malathy C (2018) Survey on diagnosis of diseases from retinal images. In: Journal of physics: conference series, IOP Publishing, vol 1000, p 012053","DOI":"10.1088\/1742-6596\/1000\/1\/012053"},{"issue":"1","key":"10736_CR81","doi-asserted-by":"publisher","first-page":"14","DOI":"10.4103\/0301-4738.178142","volume":"64","author":"T Das","year":"2016","unstructured":"Das T, Aurora A, Chhablani J, Giridhar A, Kumar A, Raman R, Nagpal M, Narayanan R, Natarajan S, Ramasamay K et al (2016) Evidence-based review of diabetic macular edema management: consensus statement on Indian treatment guidelines. Indian J Ophthalmol 64(1):14","journal-title":"Indian J Ophthalmol"},{"issue":"1","key":"10736_CR82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2019.2963712","volume":"4","author":"V Das","year":"2020","unstructured":"Das V, Dandapat S, Bora PK (2020) A data-efficient approach for automated classification of OCT images using generative adversarial network. IEEE Sens Lett 4(1):1\u20134","journal-title":"IEEE Sens Lett"},{"issue":"15","key":"10736_CR83","doi-asserted-by":"publisher","first-page":"8746","DOI":"10.1109\/JSEN.2020.2985131","volume":"20","author":"V Das","year":"2020","unstructured":"Das V, Dandapat S, Bora PK (2020) Unsupervised super-resolution of OCT images using generative adversarial network for improved age-related macular degeneration diagnosis. IEEE Sens J 20(15):8746\u20138756","journal-title":"IEEE Sens J"},{"key":"10736_CR84","unstructured":"Davson H et al. The structure of the human eye. https:\/\/www.britannica.com\/science\/human-eye. Accessed 5 Nov 2022"},{"issue":"9","key":"10736_CR85","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, Askham H, Glorot X, O\u2019Donoghue B, Visentin D et al (2018) Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 24(9):1342\u20131350","journal-title":"Nat Med"},{"key":"10736_CR86","doi-asserted-by":"crossref","unstructured":"de Jong EK, Geerlings MJ, den Hollander AI (2020) Age-related macular degeneration. Genet Genom Eye Dis 155\u2013180","DOI":"10.1016\/B978-0-12-816222-4.00010-1"},{"key":"10736_CR87","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.neucom.2018.07.102","volume":"396","author":"J de La Torre","year":"2020","unstructured":"de La Torre J, Valls A, Puig D (2020) A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing 396:465\u2013476","journal-title":"Neurocomputing"},{"issue":"8","key":"10736_CR88","first-page":"6255","volume":"34","author":"V Deepa","year":"2022","unstructured":"Deepa V, Kumar CS, Cherian T (2022) Ensemble of multi-stage deep convolutional neural networks for automated grading of diabetic retinopathy using image patches. J King Saud Univ-Comput Inf Sci 34(8):6255\u20136265","journal-title":"J King Saud Univ-Comput Inf Sci"},{"issue":"103","key":"10736_CR90","first-page":"467","volume":"73","author":"X Deng","year":"2022","unstructured":"Deng X, Ye J (2022) A retinal blood vessel segmentation based on improved D-MNet and pulse-coupled neural network. Biomed Signal Process Control 73(103):467","journal-title":"Biomed Signal Process Control"},{"issue":"7","key":"10736_CR91","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1364\/boe.9.003244","volume":"9","author":"SK Devalla","year":"2018","unstructured":"Devalla SK, Renukanand PK, Sreedhar BK, Subramanian G, Zhang L, Perera S, Mari J, Chin KS, Tun TA, Strouthidis NG, Aung T, Thi\u00e9ry AH, Girard MJA (2018) DRUNET: a dilated-residual u-net deep learning network to segment optic nerve head tissues in optical coherence tomography images. Biomed Opt Express 9(7):3244. https:\/\/doi.org\/10.1364\/boe.9.003244","journal-title":"Biomed Opt Express"},{"key":"10736_CR92","unstructured":"DiaRetDb0 (2007) Diaretdb0: standard diabetic retinopathy database calibration level 0. https:\/\/www.medicmind.tech\/retinal-image-databases"},{"key":"10736_CR93","unstructured":"DiaRetDb1 (2007) Diaretdb1: standard diabetic retinopathy database calibration level 1. https:\/\/www.medicmind.tech\/retinal-image-databases"},{"issue":"1","key":"10736_CR95","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-019-0649-y","volume":"18","author":"A Diaz-Pinto","year":"2019","unstructured":"Diaz-Pinto A, Morales S, Naranjo V, K\u00f6hler T, Mossi JM, Navea A (2019) CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomed Eng Online 18(1):1\u201319","journal-title":"Biomed Eng Online"},{"issue":"602","key":"10736_CR96","first-page":"597","volume":"12","author":"C Dieter","year":"2021","unstructured":"Dieter C, Lemos NE, Corr\u00eaa NRDF, Assmann TS, Crispim D (2021) The impact of lncRNAs in diabetes mellitus: a systematic review and in silico analyses. Front Endocrinol 12(602):597","journal-title":"Front Endocrinol"},{"key":"10736_CR97","doi-asserted-by":"crossref","unstructured":"Domalpally A, Xing B, Pak JW, Agron E, Ferris III FL, Clemons TE, Chew EY (2022) Extramacular drusen and progression of age-related macular degeneration (AMD): age-related eye disease study 2 report 30. Ophthalmol Retina","DOI":"10.1016\/j.oret.2022.08.001"},{"key":"10736_CR98","doi-asserted-by":"crossref","unstructured":"Doshi N, Oza U, Kumar P (2020) Diabetic retinopathy classification using downscaling algorithms and deep learning. In: 2020 7th international conference on signal processing and integrated networks (SPIN), IEEE, pp 950\u2013955","DOI":"10.1109\/SPIN48934.2020.9071423"},{"issue":"1","key":"10736_CR99","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.preteyeres.2007.07.005","volume":"27","author":"W Drexler","year":"2008","unstructured":"Drexler W, Fujimoto JG (2008) State-of-the-art retinal optical coherence tomography. Prog Retin Eye Res 27(1):45\u201388","journal-title":"Prog Retin Eye Res"},{"key":"10736_CR100","unstructured":"Drishti-GS (2014) Drishti-gs database. http:\/\/cvit.iiit.ac.in\/projects\/mip\/drishti-gs\/mip-dataset2\/Home.php"},{"key":"10736_CR101","unstructured":"DRIVE (2004) Drive: digital retinal images for vessel extraction. https:\/\/drive.grand-challenge.org\/"},{"key":"10736_CR102","doi-asserted-by":"publisher","first-page":"25130","DOI":"10.1109\/access.2018.2825397","volume":"6","author":"W Duan","year":"2018","unstructured":"Duan W, Zheng Y, Ding Y, Hou S, Tang Y, Xu Y, Qin M, Wu J, Shen D, Bi H (2018) A generative model for OCT retinal layer segmentation by groupwise curve alignment. IEEE Access 6:25130\u201325141. https:\/\/doi.org\/10.1109\/access.2018.2825397","journal-title":"IEEE Access"},{"key":"10736_CR103","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.cmpb.2018.05.004","volume":"162","author":"Y Elloumi","year":"2018","unstructured":"Elloumi Y, Akil M, Kehtarnavaz N (2018) A mobile computer aided system for optic nerve head detection. Comput Methods Programs Biomed 162:139\u2013148","journal-title":"Comput Methods Programs Biomed"},{"key":"10736_CR104","doi-asserted-by":"crossref","unstructured":"Elloumi Y, Mbarek MB, Boukadida R, Akil M, Bedoui MH (2021) Fast and accurate mobile-aided screening system of moderate diabetic retinopathy. In: Thirteenth international conference on machine vision. SPIE, vol 11605, pp 232\u2013240","DOI":"10.1117\/12.2588505"},{"issue":"1","key":"10736_CR105","first-page":"1","volume":"4","author":"A Elmoufidi","year":"2023","unstructured":"Elmoufidi A, Ammoun H (2023) Diabetic retinopathy prevention using efficientnetb3 architecture and fundus photography. SN Comput Sci 4(1):1\u20139","journal-title":"SN Comput Sci"},{"issue":"9","key":"10736_CR106","doi-asserted-by":"publisher","first-page":"3490","DOI":"10.3390\/s22093490","volume":"22","author":"M Elsharkawy","year":"2022","unstructured":"Elsharkawy M, Elrazzaz M, Sharafeldeen A, Alhalabi M, Khalifa F, Soliman A, Elnakib A, Mahmoud A, Ghazal M, El-Daydamony E et al (2022) The role of different retinal imaging modalities in predicting progression of diabetic retinopathy: a survey. Sensors 22(9):3490","journal-title":"Sensors"},{"key":"10736_CR107","doi-asserted-by":"crossref","unstructured":"Eton EA, Newman-Casey PA (2022) A call for health equity in diabetic care to improve eye health. JAMA Ophthalmol","DOI":"10.1001\/jamaophthalmol.2022.1436"},{"key":"10736_CR108","doi-asserted-by":"crossref","unstructured":"Fang T, Su R, Xie L, Gu Q, Li Q, Liang P, Wang T (2015) Retinal vessel landmark detection using deep learning and hessian matrix. In: 2015 8th international congress on image and signal processing (CISP), IEEE, pp 387\u2013392","DOI":"10.1109\/CISP.2015.7407910"},{"issue":"5","key":"10736_CR109","doi-asserted-by":"publisher","first-page":"2732","DOI":"10.1364\/boe.8.002732","volume":"8","author":"L Fang","year":"2017","unstructured":"Fang L, Cunefare D, Wang C, Guymer RH, Li S, Farsiu S (2017) Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Express 8(5):2732. https:\/\/doi.org\/10.1364\/boe.8.002732","journal-title":"Biomed Opt Express"},{"issue":"8","key":"10736_CR110","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1109\/TMI.2019.2898414","volume":"38","author":"L Fang","year":"2019","unstructured":"Fang L, Wang C, Li S, Rabbani H, Chen X, Liu Z (2019) Attention to lesion: lesion-aware convolutional neural network for retinal optical coherence tomography image classification. IEEE Trans Med Imaging 38(8):1959\u20131970","journal-title":"IEEE Trans Med Imaging"},{"key":"10736_CR111","doi-asserted-by":"crossref","unstructured":"Farshad A, Yeganeh Y, Gehlbach P, Navab N (2022) Y-net: A spatiospectral dual-encoder network for medical image segmentation. In: Medical image computing and computer assisted intervention\u2013MICCAI 2022: 25th international conference, Singapore, Proceedings, Part II, Springer. pp 582\u2013592","DOI":"10.1007\/978-3-031-16434-7_56"},{"key":"10736_CR112","doi-asserted-by":"crossref","unstructured":"Farsiu S, Chiu SJ, O\u2019Connell RV, Folgar FA, Yuan E, Izatt JA, Toth CA, Group AREDSASDOCTS et al (2014) Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology 121(1):162\u2013172","DOI":"10.1016\/j.ophtha.2013.07.013"},{"issue":"8","key":"10736_CR113","doi-asserted-by":"publisher","first-page":"510","DOI":"10.3390\/gels8080510","volume":"8","author":"AM Fea","year":"2022","unstructured":"Fea AM, Novarese C, Caselgrandi P, Boscia G (2022) Glaucoma treatment and hydrogel: current insights and state of the art. Gels 8(8):510","journal-title":"Gels"},{"issue":"3","key":"10736_CR114","doi-asserted-by":"publisher","first-page":"2814","DOI":"10.3390\/ijms24032814","volume":"24","author":"AM Fea","year":"2023","unstructured":"Fea AM, Ricardi F, Novarese C, Cimorosi F, Vallino V, Boscia G (2023) Precision medicine in glaucoma: artificial intelligence, biomarkers, genetics and redox state. Int J Mol Sci 24(3):2814","journal-title":"Int J Mol Sci"},{"key":"10736_CR115","doi-asserted-by":"crossref","unstructured":"Fellah KM, Tigane S, Kahloul L (2023) Diabetic retinopathy detection using deep learning. In: International symposium on modelling and implementation of complex systems. Springer, pp 234\u2013246","DOI":"10.1007\/978-3-031-18516-8_17"},{"key":"10736_CR116","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.neucom.2018.10.098","volume":"392","author":"S Feng","year":"2020","unstructured":"Feng S, Zhuo Z, Pan D, Tian Q (2020) CcNet: a cross-connected convolutional network for segmenting retinal vessels using multi-scale features. Neurocomputing 392:268\u2013276","journal-title":"Neurocomputing"},{"key":"10736_CR117","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, PMLR, pp 1126\u20131135"},{"issue":"11","key":"10736_CR118","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1109\/TMI.2018.2837012","volume":"37","author":"H Fu","year":"2018","unstructured":"Fu H, Cheng J, Xu Y, Zhang C, Wong DWK, Liu J, Cao X (2018) Disc-aware ensemble network for glaucoma screening from fundus image. IEEE Trans Med Imaging 37(11):2493\u20132501","journal-title":"IEEE Trans Med Imaging"},{"key":"10736_CR119","unstructured":"Fundus Photography Overview. https:\/\/www.opsweb.org\/page\/fundusphotography. Accessed 25 Jan 2022"},{"key":"10736_CR120","unstructured":"Fundus Photography: What You Need to Know. https:\/\/eyesoneyecare.com\/resources\/fundus-photography\/. Accessed 25 Jan 2023"},{"issue":"1","key":"10736_CR121","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1097\/IJG.0000000000001964","volume":"31","author":"RO Funk","year":"2022","unstructured":"Funk RO, Hodge DO, Kohli D, Roddy GW (2022) Multiple systemic vascular risk factors are associated with low-tension glaucoma. J Glaucoma 31(1):15\u201322","journal-title":"J Glaucoma"},{"key":"10736_CR122","doi-asserted-by":"crossref","unstructured":"Fu H, Xu Y, Wong DWK, Liu J (2016) Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, pp 698\u2013701","DOI":"10.1109\/ISBI.2016.7493362"},{"key":"10736_CR123","unstructured":"GAMMA (2021) Gamma: glaucoma grading from multi-modality images. https:\/\/gamma.grand-challenge.org\/"},{"key":"10736_CR124","doi-asserted-by":"publisher","unstructured":"Gao E, Shi F, Zhu W, Chen B, Chen H, Chen X (2014) Comparison of retinal thickness measurements of normal eyes between topcon algorithm and a graph based algorithm. In: Proceedings of the ophthalmic medical image analysis first international workshop, University of Iowa. https:\/\/doi.org\/10.17077\/omia.1012","DOI":"10.17077\/omia.1012"},{"issue":"8","key":"10736_CR125","first-page":"987","volume":"26","author":"C Garcia","year":"2020","unstructured":"Garcia C et al (2020) AI-assisted telemedicine for retinopathy of prematurity screening. Telemed J E Health 26(8):987\u2013995","journal-title":"Telemed J E Health"},{"issue":"7","key":"10736_CR126","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","volume":"124","author":"R Gargeya","year":"2017","unstructured":"Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7):962\u2013969","journal-title":"Ophthalmology"},{"issue":"104","key":"10736_CR127","first-page":"725","volume":"136","author":"A Garifullin","year":"2021","unstructured":"Garifullin A, Lensu L, Uusitalo H (2021) Deep Bayesian baseline for segmenting diabetic retinopathy lesions: advances and challenges. Comput Biol Med 136(104):725","journal-title":"Comput Biol Med"},{"issue":"1","key":"10736_CR128","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-81554-4","volume":"11","author":"S Gheisari","year":"2021","unstructured":"Gheisari S, Shariflou S, Phu J, Kennedy PJ, Agar A, Kalloniatis M, Golzan SM (2021) A combined convolutional and recurrent neural network for enhanced glaucoma detection. Sci Rep 11(1):1\u201311","journal-title":"Sci Rep"},{"key":"10736_CR129","unstructured":"Gilbert C, Jackson ML, Kyari F (2019) World report on vision"},{"issue":"5","key":"10736_CR130","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1167\/iovs.63.5.25","volume":"63","author":"Y Glidai","year":"2022","unstructured":"Glidai Y, Lucy KA, Schuman JS, Alexopoulos P, Wang B, Wu M, Liu M, Geest JPV, Kollech HG, Lee T et al (2022) Microstructural deformations within the depth of the lamina cribrosa in response to acute in vivo intraocular pressure modulation. Investig Ophthalmol Vis Sci 63(5):25","journal-title":"Investig Ophthalmol Vis Sci"},{"issue":"2","key":"10736_CR131","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1364\/BOE.10.000892","volume":"10","author":"JJ G\u00f3mez-Valverde","year":"2019","unstructured":"G\u00f3mez-Valverde JJ, Ant\u00f3n A, Fatti G, Liefers B, Herranz A, Santos A, S\u00e1nchez CI, Ledesma-Carbayo MJ (2019) Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomed Opt Express 10(2):892\u2013913","journal-title":"Biomed Opt Express"},{"issue":"7","key":"10736_CR132","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.3390\/pharmaceutics14071405","volume":"14","author":"MA Gonz\u00e1lez-Cela-Casamayor","year":"2022","unstructured":"Gonz\u00e1lez-Cela-Casamayor MA, L\u00f3pez-Cano JJ, Bravo-Osuna I, Andr\u00e9s-Guerrero V, Vicario-de-la Torre M, Guzm\u00e1n-Navarro M, Ben\u00edtez-del Castillo JM, Herrero-Vanrell R, Molina-Mart\u00ednez IT (2022) Novel osmoprotective dopc-dmpc liposomes loaded with antihypertensive drugs as potential strategy for glaucoma treatment. Pharmaceutics 14(7):1405","journal-title":"Pharmaceutics"},{"key":"10736_CR133","doi-asserted-by":"crossref","unstructured":"Gopinath K, Rangrej SB, Sivaswamy J (2017) A deep learning framework for segmentation of retinal layers from OCT images. In: 2017 4th IAPR Asian conference on pattern recognition (ACPR). IEEE, pp 888\u2013893","DOI":"10.1109\/ACPR.2017.121"},{"issue":"1","key":"10736_CR134","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1016\/j.jse.2021.06.016","volume":"31","author":"JA Gordon","year":"2022","unstructured":"Gordon JA, Farooqi AS, Rabut E, Huffman GR, Schug J, Kelly JD, Dodge GR (2022) Evaluating whole-genome expression differences in idiopathic and diabetic adhesive capsulitis. J Shoulder Elbow Surg 31(1):e1\u2013e13","journal-title":"J Shoulder Elbow Surg"},{"issue":"7","key":"10736_CR135","first-page":"2726","volume":"63","author":"C Guan","year":"2022","unstructured":"Guan C, Ling YTT, Pease M, Quillen S, Johnson TV, Nguyen TD, Quigley HA (2022) Structure of astrocytes, axons, and lamina cribrosa beams in human glaucoma. Investig Ophthalmol Vis Sci 63(7):2726-A0090","journal-title":"Investig Ophthalmol Vis Sci"},{"key":"10736_CR136","doi-asserted-by":"crossref","unstructured":"Gulati P, Dhiman N, Singh T, Chauhan A (2022) Detection of haemorrhages and microaneurysms iris disease using few shot learning. In: AIP conference proceedings. AIP Publishing LLC, vol 2481, p 020025","DOI":"10.1063\/5.0103825"},{"issue":"22","key":"10736_CR137","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402\u20132410","journal-title":"JAMA"},{"key":"10736_CR138","unstructured":"Gupta S, Karandikar A (2015) A survey on methods of automatic detection of diabetic retinopathy. Int J Res IT Manag Eng 5(1)"},{"key":"10736_CR139","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1146\/annurev-publhealth-040218-044158","volume":"40","author":"D Haire-Joshu","year":"2019","unstructured":"Haire-Joshu D, Hill-Briggs F (2019) The next generation of diabetes translation: a path to health equity. Annu Rev Public Health 40:391\u2013410","journal-title":"Annu Rev Public Health"},{"key":"10736_CR140","doi-asserted-by":"crossref","unstructured":"Hamel AR, Rouhana JM, Yan W, Monavarfeshani A, Jiang X, Liang Q, Mehta PA, Wang J, Shrivastava A, Duchinski K et al (2022) Integrating genetic regulation and single-cell expression with GWAS prioritizes causal genes and cell types for glaucoma. medRxiv","DOI":"10.1101\/2022.05.14.22275022"},{"key":"10736_CR141","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.ajo.2017.03.008","volume":"178","author":"N Hammel","year":"2017","unstructured":"Hammel N, Belghith A, Weinreb RN, Medeiros FA, Mendoza N, Zangwill LM (2017) Comparing the rates of retinal nerve fiber layer and ganglion cell-inner plexiform layer loss in healthy eyes and in glaucoma eyes. Am J Ophthalmol 178:38\u201350","journal-title":"Am J Ophthalmol"},{"key":"10736_CR143","doi-asserted-by":"crossref","unstructured":"Hassan B, Raja G (2016) Fully automated assessment of macular edema using optical coherence tomography (oct) images. In: 2016 international conference on intelligent systems engineering (ICISE), IEEE, pp 5\u20139","DOI":"10.1109\/INTELSE.2016.7475153"},{"key":"10736_CR144","doi-asserted-by":"crossref","unstructured":"Hassan T, Akram MU, Hassan B, Nasim A, Bazaz SA (2015) Review of OCT and fundus images for detection of Macular Edema. IEEE international conference on imaging systems and techniques (IST), pp 1\u20134","DOI":"10.1109\/IST.2015.7294517"},{"issue":"4","key":"10736_CR145","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1364\/JOSAA.33.000455","volume":"33","author":"B Hassan","year":"2016","unstructured":"Hassan B, Raja G, Hassan T, Akram MU (2016) Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images. JOSA A 33(4):455\u2013463","journal-title":"JOSA A"},{"issue":"3","key":"10736_CR146","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1364\/AO.55.000454","volume":"55","author":"T Hassan","year":"2016","unstructured":"Hassan T, Akram MU, Hassan B, Syed AM, Bazaz SA (2016) Automated segmentation of subretinal layers for the detection of macular edema. Appl Opt 55(3):454\u2013461","journal-title":"Appl Opt"},{"issue":"11","key":"10736_CR147","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-1078-3","volume":"42","author":"T Hassan","year":"2018","unstructured":"Hassan T, Akram MU, Akhtar M, Khan SA, Yasin U (2018) Multilayered deep structure tensor Delaunay triangulation and morphing based automated diagnosis and 3d presentation of human macula. J Med Syst 42(11):1\u201317","journal-title":"J Med Syst"},{"key":"10736_CR148","doi-asserted-by":"crossref","unstructured":"Hassan T, Akram MU, Masood MF, Yasin U (2018b) Biomisa retinal image database for macular and ocular syndromes. In: Image analysis and recognition: 15th international conference, ICIAR 2018, P\u00f3voa de Varzim, Proceedings 15, Springer, pp 695\u2013705","DOI":"10.1007\/978-3-319-93000-8_79"},{"issue":"1","key":"10736_CR149","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/JBHI.2020.2982914","volume":"25","author":"T Hassan","year":"2020","unstructured":"Hassan T, Akram MU, Werghi N, Nazir MN (2020) RAG-FW: A hybrid convolutional framework for the automated extraction of retinal lesions and lesion-influenced grading of human retinal pathology. IEEE J Biomed Health Inform 25(1):108\u2013120","journal-title":"IEEE J Biomed Health Inform"},{"key":"10736_CR150","first-page":"1","volume":"70","author":"T Hassan","year":"2021","unstructured":"Hassan T, Hassan B, Akram MU, Hashmi S, Taguri AH, Werghi N (2021) Incremental cross-domain adaptation for robust retinopathy screening via Bayesian deep learning. IEEE Trans Instrum Meas 70:1\u201314","journal-title":"IEEE Trans Instrum Meas"},{"key":"10736_CR151","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.inffus.2022.12.006","volume":"92","author":"T Hassan","year":"2023","unstructured":"Hassan T, Li Z, Akram MU, Hussain I, Khalaf K, Werghi N (2023) Angular contrastive distillation driven self-supervised scanner independent screening and grading of retinopathy. Inf Fusion 92:404\u2013419","journal-title":"Inf Fusion"},{"key":"10736_CR152","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D (2022) Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. In: Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries: 7th international workshop, BrainLes 2021, held in conjunction with MICCAI 2021, Virtual Event, Revised Selected Papers, Part I. Springer, pp 272\u2013284","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"10736_CR153","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.inffus.2021.02.017","volume":"73","author":"W He","year":"2021","unstructured":"He W, Wang X, Wang L, Huang Y, Yang Z, Yao X, Zhao X, Ju L, Wu L, Wu L et al (2021) Incremental learning for exudate and hemorrhage segmentation on fundus images. Inf Fusion 73:157\u2013164","journal-title":"Inf Fusion"},{"issue":"101","key":"10736_CR154","first-page":"856","volume":"68","author":"Y He","year":"2021","unstructured":"He Y, Carass A, Liu Y, Jedynak BM, Solomon SD, Saidha S, Calabresi PA, Prince JL (2021) Structured layer surface segmentation for retina OCT using fully convolutional regression networks. Med Image Anal 68(101):856","journal-title":"Med Image Anal"},{"issue":"879","key":"10736_CR155","first-page":"957","volume":"10","author":"M He","year":"2022","unstructured":"He M, Rong R, Ji D, Xia X (2022) From bench to bed: the current genome editing therapies for glaucoma. Front Cell Dev Biol 10(879):957","journal-title":"Front Cell Dev Biol"},{"issue":"2","key":"10736_CR156","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1111\/opo.12675","volume":"40","author":"TJ Heesterbeek","year":"2020","unstructured":"Heesterbeek TJ, Lor\u00e9s-Motta L, Hoyng CB, Lechanteur YT, den Hollander AI (2020) Risk factors for progression of age-related macular degeneration. Ophthalmic Physiol Opt 40(2):140\u2013170","journal-title":"Ophthalmic Physiol Opt"},{"key":"10736_CR157","unstructured":"He H, Lin L, Cai Z, Tang X (2022a) Joined: prior guided multi-task learning for joint optic disc\/cup segmentation and fovea detection. Preprint arXiv:2203.00461"},{"issue":"108","key":"10736_CR158","first-page":"347","volume":"116","author":"\u00c1S Hervella","year":"2022","unstructured":"Hervella \u00c1S, Rouco J, Novo J, Ortega M (2022) End-to-end multi-task learning for simultaneous optic disc and cup segmentation and glaucoma classification in eye fundus images. Appl Soft Comput 116(108):347","journal-title":"Appl Soft Comput"},{"issue":"8","key":"10736_CR160","doi-asserted-by":"publisher","first-page":"2430","DOI":"10.2337\/dc13-1161","volume":"36","author":"JO Hill","year":"2013","unstructured":"Hill JO, Galloway JM, Goley A, Marrero DG, Minners R, Montgomery B, Peterson GE, Ratner RE, Sanchez E, Aroda VR (2013) Scientific statement: socioecological determinants of prediabetes and type 2 diabetes. Diabetes Care 36(8):2430\u20132439","journal-title":"Diabetes Care"},{"issue":"1","key":"10736_CR161","doi-asserted-by":"publisher","first-page":"258","DOI":"10.2337\/dci20-0053","volume":"44","author":"F Hill-Briggs","year":"2021","unstructured":"Hill-Briggs F, Adler NE, Berkowitz SA, Chin MH, Gary-Webb TL, Navas-Acien A, Thornton PL, Haire-Joshu D (2021) Social determinants of health and diabetes: a scientific review. Diabetes Care 44(1):258\u2013279","journal-title":"Diabetes Care"},{"key":"10736_CR162","doi-asserted-by":"crossref","unstructured":"Holland R, Leingang O, Holmes C, Anders P, Paetzold JC, Kaye R, Riedl S, Bogunovi\u0107 H, Schmidt-Erfurth U, Fritsche L et al (2023) Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration. Preprint arXiv:2301.04525","DOI":"10.1007\/978-3-031-43990-2_68"},{"key":"10736_CR163","doi-asserted-by":"crossref","unstructured":"Hou Q, Cao P, Wang J, Liu X, Yang J, Zaiane OR (2023) Self-supervised domain adaptation for breaking the limits of low-quality fundus image quality enhancement. Preprint arXiv:2301.06943","DOI":"10.1145\/3581783.3612049"},{"issue":"6","key":"10736_CR164","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.3390\/biomedicines10061269","volume":"10","author":"HY Hsu","year":"2022","unstructured":"Hsu HY, Chou YB, Jheng YC, Kao ZK, Huang HY, Chen HR, Hwang DK, Chen SJ, Chiou SH, Wu YT (2022) Automatic segmentation of retinal fluid and photoreceptor layer from optical coherence tomography images of diabetic macular edema patients using deep learning and associations with visual acuity. Biomedicines 10(6):1269","journal-title":"Biomedicines"},{"issue":"4","key":"10736_CR165","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1161\/STROKEAHA.109.571000","volume":"41","author":"CC Hu","year":"2010","unstructured":"Hu CC, Ho JD, Lin HC (2010) Neovascular age-related macular degeneration and the risk of stroke: a 5-year population-based follow-up study. Stroke 41(4):613\u2013617","journal-title":"Stroke"},{"issue":"1","key":"10736_CR166","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1080\/21655979.2016.1227144","volume":"8","author":"M Hu","year":"2017","unstructured":"Hu M, Zhu C, Li X, Xu Y (2017) Optic cup segmentation from fundus images for glaucoma diagnosis. Bioengineered 8(1):21\u201328","journal-title":"Bioengineered"},{"key":"10736_CR167","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.neucom.2018.05.011","volume":"309","author":"K Hu","year":"2018","unstructured":"Hu K, Zhang Z, Niu X, Zhang Y, Cao C, Xiao F, Gao X (2018) Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309:179\u2013191","journal-title":"Neurocomputing"},{"key":"10736_CR168","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2019.07.079","volume":"365","author":"K Hu","year":"2019","unstructured":"Hu K, Shen B, Zhang Y, Cao C, Xiao F, Gao X (2019) Automatic segmentation of retinal layer boundaries in OCT images using multiscale convolutional neural network and graph search. Neurocomputing 365:302\u2013313","journal-title":"Neurocomputing"},{"key":"10736_CR169","first-page":"1","volume":"70","author":"K Hu","year":"2021","unstructured":"Hu K, Liu D, Chen Z, Li X, Zhang Y, Gao X (2021) Embedded residual recurrent network and graph search for the segmentation of retinal layer boundaries in optical coherence tomography. IEEE Trans Instrum Meas 70:1\u201317","journal-title":"IEEE Trans Instrum Meas"},{"key":"10736_CR170","unstructured":"Huang D (2009) Oct terminology\u2014demystified! Ophthalmol Manag 13(4)"},{"issue":"109","key":"10736_CR172","first-page":"152","volume":"222","author":"C Huang","year":"2022","unstructured":"Huang C, Qi P, Cui H, Lu Q, Gao X (2022) CircFAT1 regulates retinal pigment epithelial cell pyroptosis and autophagy via mediating m6A reader protein YTHDF2 expression in diabetic retinopathy. Exp Eye Res 222(109):152","journal-title":"Exp Eye Res"},{"key":"10736_CR173","doi-asserted-by":"crossref","unstructured":"Huang H, Jansonius NM, Chen H, Los LI (2022b) Hyperreflective dots on OCT as a predictor of treatment outcome in diabetic macular edema: a systematic review. Ophthalmol Retina","DOI":"10.1016\/j.oret.2022.03.020"},{"issue":"103","key":"10736_CR174","first-page":"613","volume":"75","author":"Z Huang","year":"2022","unstructured":"Huang Z, Sun M, Liu Y, Wu J (2022) CSAUNet: a cascade self-attention u-shaped network for precise fundus vessel segmentation. Biomed Signal Process Control 75(103):613","journal-title":"Biomed Signal Process Control"},{"key":"10736_CR175","doi-asserted-by":"crossref","unstructured":"Huda SA, Ila IJ, Sarder S, Shamsujjoha M, Ali MNY (2019) An improved approach for detection of diabetic retinopathy using feature importance and machine learning algorithms. In: 2019 7th international conference on smart computing & communications (ICSCC), IEEE, pp 1\u20135","DOI":"10.1109\/ICSCC.2019.8843676"},{"issue":"4","key":"10736_CR176","doi-asserted-by":"publisher","first-page":"2205","DOI":"10.3390\/app13042205","volume":"13","author":"V Iannucci","year":"2023","unstructured":"Iannucci V, Manni P, Mecarelli G, Giammaria S, Giovannetti F, Lambiase A, Bruscolini A (2023) Childhood uveitic glaucoma: complex management in a fragile population. Appl Sci 13(4):2205","journal-title":"Appl Sci"},{"issue":"9","key":"10736_CR177","doi-asserted-by":"publisher","first-page":"e0184301","DOI":"10.1371\/journal.pone.0184301","volume":"12","author":"R Igarashi","year":"2017","unstructured":"Igarashi R, Ochiai S, Sakaue Y, Suetake A, Iikawa R, Togano T, Miyamoto F, Miyamoto D, Fukuchi T (2017) Optical coherence tomography angiography of the peripapillary capillaries in primary open-angle and normal-tension glaucoma. PLoS One 12(9):e0184301","journal-title":"PLoS One"},{"issue":"6","key":"10736_CR178","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1161\/STROKEAHA.112.654632","volume":"43","author":"MK Ikram","year":"2012","unstructured":"Ikram MK, Mitchell P, Klein R, Sharrett AR, Couper DJ, Wong TY (2012) Age-related macular degeneration and long-term risk of stroke subtypes. Stroke 43(6):1681\u20131683","journal-title":"Stroke"},{"key":"10736_CR179","doi-asserted-by":"crossref","first-page":"100865","DOI":"10.1016\/j.preteyeres.2020.100865","volume":"79","author":"Y Ikuno","year":"2020","unstructured":"Ikuno Y et al (2020) Posterior staphyloma in pathologic myopia. Prog Retin Eye Res 79:100865","journal-title":"Prog Retin Eye Res"},{"key":"10736_CR180","doi-asserted-by":"crossref","unstructured":"Im JH, Jin YP, Chow R, Yan P (2022) Prevalence of diabetic macular edema based on optical coherence tomography in people with diabetes: a systematic review and meta-analysis. Surv Ophthalmol","DOI":"10.1016\/j.survophthal.2022.01.009"},{"key":"10736_CR181","doi-asserted-by":"publisher","first-page":"105602","DOI":"10.1016\/j.compbiomed.2022.105602","volume":"146","author":"MR Islam","year":"2022","unstructured":"Islam MR, Abdulrazak LF, Nahiduzzaman M, Goni MOF, Anower MS, Ahsan M, Haider J, Kowalski M (2022) Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images. Comput Biol Med 146:105602","journal-title":"Comput Biol Med"},{"issue":"2","key":"10736_CR182","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.cmpb.2015.08.002","volume":"122","author":"A Issac","year":"2015","unstructured":"Issac A, Sarathi MP, Dutta MK (2015) An adaptive threshold based image processing technique for improved glaucoma detection and classification. Comput Methods Programs Biomed 122(2):229\u2013244","journal-title":"Comput Methods Programs Biomed"},{"key":"10736_CR183","unstructured":"Jebaseeli J (2021) The prediction of diabetic retinopathy using machine learning techniques. J Eng Res"},{"issue":"9","key":"10736_CR184","doi-asserted-by":"publisher","first-page":"2107","DOI":"10.1109\/TMI.2016.2550102","volume":"35","author":"T Jerman","year":"2016","unstructured":"Jerman T, Pernu\u0161 F, Likar B, \u0160piclin \u017d (2016) Enhancement of vascular structures in 3d and 2d angiographic images. IEEE Trans Med Imaging 35(9):2107\u20132118","journal-title":"IEEE Trans Med Imaging"},{"key":"10736_CR185","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2018.04.005","volume":"68","author":"Z Jiang","year":"2018","unstructured":"Jiang Z, Zhang H, Wang Y, Ko SB (2018) Retinal blood vessel segmentation using fully convolutional network with transfer learning. Comput Med Imaging Graph 68:1\u201315","journal-title":"Comput Med Imaging Graph"},{"key":"10736_CR186","doi-asserted-by":"publisher","first-page":"64483","DOI":"10.1109\/ACCESS.2019.2917508","volume":"7","author":"Y Jiang","year":"2019","unstructured":"Jiang Y, Tan N, Peng T (2019) Optic disc and cup segmentation based on deep convolutional generative adversarial networks. IEEE Access 7:64483\u201364493","journal-title":"IEEE Access"},{"issue":"4","key":"10736_CR187","doi-asserted-by":"publisher","first-page":"e054420","DOI":"10.1136\/bmjopen-2021-054420","volume":"12","author":"J Jiang","year":"2022","unstructured":"Jiang J, Chen Y, Zhang H, Yuan W, Zhao T, Wang N, Fan G, Zheng D, Wang Z (2022) Association between metformin use and the risk of age-related macular degeneration in patients with type 2 diabetes: a retrospective study. BMJ open 12(4):e054420","journal-title":"BMJ open"},{"issue":"7","key":"10736_CR189","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.3390\/pharmaceutics14071473","volume":"14","author":"Y Jim\u00e9nez-G\u00f3mez","year":"2022","unstructured":"Jim\u00e9nez-G\u00f3mez Y, Alba-Molina D, Blanco-Blanco M, P\u00e9rez-Fajardo L, Reyes-Ortega F, Ortega-Llamas L, Villalba-Gonz\u00e1lez M, Fern\u00e1ndez-Choquet de Isla I, Pugliese F, Stoikow I et al (2022) Novel treatments for age-related macular degeneration: a review of clinical advances in sustained drug delivery systems. Pharmaceutics 14(7):1473","journal-title":"Pharmaceutics"},{"issue":"2","key":"10736_CR190","doi-asserted-by":"publisher","first-page":"e512","DOI":"10.1111\/aos.14928","volume":"100","author":"K Jin","year":"2022","unstructured":"Jin K, Yan Y, Chen M, Wang J, Pan X, Liu X, Liu M, Lou L, Wang Y, Ye J (2022) Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age-related macular degeneration. Acta Ophthalmol 100(2):e512\u2013e520","journal-title":"Acta Ophthalmol"},{"key":"10736_CR191","doi-asserted-by":"crossref","unstructured":"Jiwane V, DattaGupta A, Chauhan A, Patil V (2022) Detecting diabetic retinopathy using deep learning technique with resnet-50. In: ICDSMLA 2020. Springer, pp 45\u201355","DOI":"10.1007\/978-981-16-3690-5_5"},{"issue":"1","key":"10736_CR192","first-page":"45","volume":"42","author":"B Jones","year":"2022","unstructured":"Jones B et al (2022) Multi-modal imaging in retinopathy of prematurity: a comprehensive approach. Ophthalmic Res 42(1):45\u201352","journal-title":"Ophthalmic Res"},{"key":"10736_CR193","doi-asserted-by":"crossref","unstructured":"Joshua AO, Nelwamondo FV, Mabuza-Hocquet G (2019) Segmentation of optic cup and disc for diagnosis of glaucoma on retinal fundus images. In: 2019 Southern African universities power engineering conference\/robotics and mechatronics\/pattern recognition association of South Africa (SAUPEC\/RobMech\/PRASA), IEEE, pp 183\u2013187","DOI":"10.1109\/RoboMech.2019.8704727"},{"issue":"1","key":"10736_CR194","doi-asserted-by":"publisher","first-page":"45","DOI":"10.4103\/2228-7477.114321","volume":"3","author":"R Kafieh","year":"2013","unstructured":"Kafieh R, Rabbani H, Kermani S (2013) A review of algorithms for segmentation of optical coherence tomography from retina. J Med Signals Sens 3(1):45","journal-title":"J Med Signals Sens"},{"key":"10736_CR195","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/259123","volume":"2015","author":"R Kafieh","year":"2015","unstructured":"Kafieh R, Rabbani H, Hajizadeh F, Abramoff MD, Sonka M (2015) Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes. J Ophthalmol 2015:1\u201314. https:\/\/doi.org\/10.1155\/2015\/259123","journal-title":"J Ophthalmol"},{"key":"10736_CR196","unstructured":"Kaggle-DR (2015) Kaggle: diabetic retinopathy detection. https:\/\/www.kaggle.com\/competitions\/diabetic-retinopathy-detection\/data"},{"issue":"2","key":"10736_CR197","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.cmpb.2014.08.003","volume":"117","author":"EF Kao","year":"2014","unstructured":"Kao EF, Lin PC, Chou MC, Jaw TS, Liu GC (2014) Automated detection of fovea in fundus images based on vessel-free zone and adaptive Gaussian template. Comput Methods Programs Biomed 117(2):92\u2013103","journal-title":"Comput Methods Programs Biomed"},{"key":"10736_CR198","doi-asserted-by":"crossref","unstructured":"Kaplan S, Lensu L (2022) Contrastive learning for generating optical coherence tomography images of the retina. In: Simulation and synthesis in medical imaging: 7th international workshop, SASHIMI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings, Springer, pp 112\u2013121","DOI":"10.1007\/978-3-031-16980-9_11"},{"key":"10736_CR199","doi-asserted-by":"crossref","unstructured":"Kar MK, Neog DR, Nath MK (2022a) Retinal vessel segmentation using multi-scale residual convolutional neural network (MSR-Net) combined with generative adversarial networks. Circuits Syst Signal Process 1\u201330","DOI":"10.1007\/s00034-022-02190-5"},{"issue":"2","key":"10736_CR200","doi-asserted-by":"publisher","first-page":"100123","DOI":"10.1016\/j.xops.2022.100123","volume":"2","author":"SS Kar","year":"2022","unstructured":"Kar SS, Abraham J, Wykoff CC, Sevgi DD, Lunasco L, Brown DM, Srivastava SK, Madabhushi A, Ehlers JP (2022) Computational imaging biomarker correlation with intraocular cytokine expression in diabetic macular edema: radiomics insights from the imagine study. Ophthalmol Sci 2(2):100123","journal-title":"Ophthalmol Sci"},{"issue":"2","key":"10736_CR202","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1111\/ceo.14039","volume":"50","author":"T Karaconji","year":"2022","unstructured":"Karaconji T, Zagora S, Grigg JR (2022) Approach to childhood glaucoma: a review. Clin Exp Ophthalmol 50(2):232\u2013246","journal-title":"Clin Exp Ophthalmol"},{"key":"10736_CR203","doi-asserted-by":"crossref","unstructured":"Karki SS, Kulkarni P (2021) Diabetic retinopathy classification using a combination of efficientnets. In: 2021 international conference on emerging smart computing and informatics (ESCI). IEEE, pp 68\u201372","DOI":"10.1109\/ESCI50559.2021.9397035"},{"issue":"2","key":"10736_CR204","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1364\/BOE.8.000579","volume":"8","author":"SPK Karri","year":"2017","unstructured":"Karri SPK, Chakraborty D, Chatterjee J (2017) Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed Opt Express 8(2):579\u2013592","journal-title":"Biomed Opt Express"},{"issue":"10","key":"10736_CR205","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.1136\/bjophthalmol-2019-314795","volume":"104","author":"E Karvonen","year":"2020","unstructured":"Karvonen E, Stoor K, Luodonp\u00e4\u00e4 M, H\u00e4gg P, Lintonen T, Liinamaa J, Tuulonen A, Saarela V (2020) Diagnostic performance of modern imaging instruments in glaucoma screening. Br J Ophthalmol 104(10):1399\u20131405","journal-title":"Br J Ophthalmol"},{"issue":"1","key":"10736_CR207","doi-asserted-by":"crossref","first-page":"19672","DOI":"10.1038\/s41598-019-56271-8","volume":"9","author":"S Keel","year":"2019","unstructured":"Keel S, Lee PY, Scheetz J, Li Z, Kotowicz MA, MacIsaac RJ (2019) Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep 9(1):19672","journal-title":"Sci Rep"},{"issue":"10\u201317","key":"10736_CR209","first-page":"632","volume":"3","author":"D Kermany","year":"2018","unstructured":"Kermany D, Zhang K, Goldbaum M (2018) Large dataset of labeled optical coherence tomography (OCT) and chest x-ray images. Mendeley Data 3(10\u201317):632","journal-title":"Mendeley Data"},{"issue":"5","key":"10736_CR210","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122\u20131131","journal-title":"Cell"},{"key":"10736_CR211","doi-asserted-by":"crossref","unstructured":"Khalid S, Akram MU, Hassan T, Nasim A, Jameel A (2017) Fully automated robust system to detect retinal edema, central serous chorioretinopathy, and age related macular degeneration from optical coherence tomography images. BioMed research international 2017","DOI":"10.1155\/2017\/7148245"},{"key":"10736_CR212","doi-asserted-by":"publisher","unstructured":"Khalil T, Khalid S, Syed AM (2014) Review of machine learning techniques for glaucoma detection and prediction. In: 2014 science and information conference. pp 438-442. https:\/\/doi.org\/10.1109\/SAI.2014.6918224","DOI":"10.1109\/SAI.2014.6918224"},{"key":"10736_CR213","doi-asserted-by":"publisher","first-page":"4560","DOI":"10.1109\/access.2018.2791427","volume":"6","author":"T Khalil","year":"2018","unstructured":"Khalil T, Akram MU, Raja H, Jameel A, Basit I (2018) Detection of glaucoma using cup to disc ratio from spectral domain optical coherence tomography images. IEEE Access 6:4560\u20134576. https:\/\/doi.org\/10.1109\/access.2018.2791427","journal-title":"IEEE Access"},{"issue":"1","key":"10736_CR214","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1177\/11206721221113681","volume":"33","author":"MR Khalili","year":"2023","unstructured":"Khalili MR, Bremner F, Tabrizi R, Bashi A (2023) Optical coherence tomography angiography (OCT angiography) in anterior ischemic optic neuropathy (AION): a systematic review and meta-analysis. Eur J Ophthalmol 33(1):530\u2013545","journal-title":"Eur J Ophthalmol"},{"issue":"3","key":"10736_CR215","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1007\/s10044-018-0754-8","volume":"22","author":"KB Khan","year":"2019","unstructured":"Khan KB, Khaliq AA, Jalil A, Iftikhar MA, Ullah N, Aziz MW, Ullah K, Shahid M (2019) A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends. Pattern Anal Appl 22(3):767\u2013802","journal-title":"Pattern Anal Appl"},{"key":"10736_CR216","doi-asserted-by":"crossref","unstructured":"Kim M, Zuallaert J, De Neve W (2017) Few-shot learning using a small-sized dataset of high-resolution fundus images for glaucoma diagnosis. In: Proceedings of the 2nd international workshop on multimedia for personal health and health care, pp 89\u201392","DOI":"10.1145\/3132635.3132650"},{"issue":"5","key":"10736_CR217","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/j.ophtha.2018.12.042","volume":"126","author":"JA Kim","year":"2019","unstructured":"Kim JA, Kim TW, Lee EJ, Girard MJ, Mari JM (2019) Lamina cribrosa morphology in glaucomatous eyes with hemifield defect in a Korean population. Ophthalmology 126(5):692\u2013701","journal-title":"Ophthalmology"},{"issue":"15","key":"10736_CR218","doi-asserted-by":"publisher","first-page":"3064","DOI":"10.3390\/app9153064","volume":"9","author":"M Kim","year":"2019","unstructured":"Kim M, Han JC, Hyun SH, Janssens O, Van Hoecke S, Kee C, De Neve W (2019) Medinoid: computer-aided diagnosis and localization of glaucoma using deep learning. Appl Sci 9(15):3064","journal-title":"Appl Sci"},{"issue":"5","key":"10736_CR219","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1167\/iovs.61.5.9","volume":"61","author":"GN Kim","year":"2020","unstructured":"Kim GN, Kim JA, Kim MJ, Lee EJ, Hwang JM, Kim TW (2020) Comparison of lamina cribrosa morphology in normal tension glaucoma and autosomal-dominant optic atrophy. Investig Ophthalmol Vis Sci 61(5):9","journal-title":"Investig Ophthalmol Vis Sci"},{"issue":"2","key":"10736_CR220","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1167\/iovs.63.2.23","volume":"63","author":"JA Kim","year":"2022","unstructured":"Kim JA, Lee SH, Son DH, Kim TW, Lee EJ, Girard MJ, Mari JM (2022) Morphologic changes in the lamina cribrosa upon intraocular pressure lowering in patients with normal tension glaucoma. Investig Ophthalmol Vis Sci 63(2):23","journal-title":"Investig Ophthalmol Vis Sci"},{"key":"10736_CR221","doi-asserted-by":"crossref","unstructured":"Kj\u00e6rsgaard M, Grauslund J, Vestergaard AH, Subhi Y (2022) Relationship between diabetic retinopathy and primary open-angle glaucoma: a systematic review and meta-analysis. Ophthalmic Res","DOI":"10.1159\/000523940"},{"issue":"1","key":"10736_CR222","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0161-6420(84)34337-8","volume":"91","author":"R Klein","year":"1984","unstructured":"Klein R, Klein BE, Moss SE (1984) Visual impairment in diabetes. Ophthalmology 91(1):1\u20139","journal-title":"Ophthalmology"},{"key":"10736_CR223","doi-asserted-by":"crossref","unstructured":"Kolli A, Sekimitsu S, Wang J, Segre A, Friedman D, Elze T, Pasquale LR, Wiggs J, Zebardast N (2022) Background polygenic risk modulates the association between glaucoma and cardiopulmonary diseases and measures: an analysis from the UK biobank. Br J Ophthalmol","DOI":"10.1136\/bjophthalmol-2021-320305"},{"issue":"6","key":"10736_CR224","doi-asserted-by":"publisher","first-page":"395","DOI":"10.5405\/jmbe.724","volume":"31","author":"C Kose","year":"2011","unstructured":"Kose C, Ikibacs C (2011) Statistical techniques for detection of optic disc and macula and parameters measurement in retinal fundus images. J Med Biol Eng 31(6):395\u2013404","journal-title":"J Med Biol Eng"},{"issue":"1","key":"10736_CR225","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1038\/s41597-022-01388-1","volume":"9","author":"O Kovalyk","year":"2022","unstructured":"Kovalyk O, Morales-S\u00e1nchez J, Verd\u00fa-Monedero R, Sell\u00e9s-Navarro I, Palaz\u00f3n-Cabanes A, Sancho-G\u00f3mez JL (2022) Papila: dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment. Sci Data 9(1):291","journal-title":"Sci Data"},{"issue":"7","key":"10736_CR226","first-page":"1","volume":"1","author":"R Kromer","year":"2017","unstructured":"Kromer R, Rahman S, Filev F, Klemm M (2017) An approach for automated segmentation of retinal layers in peripapillary spectralis SD-OCT images using curve regularisation. Insights Ophthalmol 1(7):1\u20136","journal-title":"Insights Ophthalmol"},{"issue":"10","key":"10736_CR227","doi-asserted-by":"publisher","first-page":"2002","DOI":"10.1016\/j.ophtha.2015.06.015","volume":"122","author":"TM Kuang","year":"2015","unstructured":"Kuang TM, Zhang C, Zangwill LM, Weinreb RN, Medeiros FA (2015) Estimating lead time gained by optical coherence tomography in detecting glaucoma before development of visual field defects. Ophthalmology 122(10):2002\u20132009","journal-title":"Ophthalmology"},{"issue":"1","key":"10736_CR228","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-11220-w","volume":"12","author":"A Kulshrestha","year":"2022","unstructured":"Kulshrestha A, Singh N, Moharana B, Gupta PC, Ram J, Singh R (2022) Axial myopia, a protective factor for diabetic retinopathy-role of vascular endothelial growth factor. Sci Rep 12(1):1\u20136","journal-title":"Sci Rep"},{"issue":"103","key":"10736_CR229","first-page":"089","volume":"71","author":"R Kumar","year":"2022","unstructured":"Kumar R, Bhandari AK (2022) Luminosity and contrast enhancement of retinal vessel images using weighted average histogram. Biomed Signal Process Control 71(103):089","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"10736_CR230","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-021-00169-1","volume":"10","author":"R Kumar","year":"2022","unstructured":"Kumar R, Gupta M et al (2022) Optical coherence tomography image based eye disease detection using deep convolutional neural network. Health Inf Sci Syst 10(1):1\u201316","journal-title":"Health Inf Sci Syst"},{"key":"10736_CR231","doi-asserted-by":"crossref","unstructured":"Kumari CU, Hemanth A, Anand V, Kumar DS, Sanjeev RN, Harshitha TSS (2022) Deep learning based detection of diabetic retinopathy using retinal fundus images. In: 2022 third international conference on intelligent computing instrumentation and control technologies (ICICICT), IEEE, pp 1312\u20131316","DOI":"10.1109\/ICICICT54557.2022.9917709"},{"issue":"10","key":"10736_CR232","first-page":"1","volume":"19","author":"CC Kwan","year":"2019","unstructured":"Kwan CC, Fawzi AA (2019) Imaging and biomarkers in diabetic macular edema and diabetic retinopathy. Curr DiabRep 19(10):1\u201310","journal-title":"Curr DiabRep"},{"issue":"9","key":"10736_CR233","doi-asserted-by":"publisher","first-page":"165","DOI":"10.3390\/jimaging7090165","volume":"7","author":"V Lakshminarayanan","year":"2021","unstructured":"Lakshminarayanan V, Kheradfallah H, Sarkar A, Jothi Balaji J (2021) Automated detection and diagnosis of diabetic retinopathy: a comprehensive survey. J Imaging 7(9):165","journal-title":"J Imaging"},{"issue":"4","key":"10736_CR234","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-022-04984-3","volume":"4","author":"J Latif","year":"2022","unstructured":"Latif J, Tu S, Xiao C, Ur Rehman S, Imran A, Latif Y (2022) Odgnet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images. SN Appl Sci 4(4):1\u201311","journal-title":"SN Appl Sci"},{"issue":"10","key":"10736_CR235","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1530\/EC-21-0328","volume":"10","author":"L Lei","year":"2021","unstructured":"Lei L, Bai YH, Jiang HY, He T, Li M, Wang JP (2021) A bioinformatics analysis of the contribution of m6a methylation to the occurrence of diabetes mellitus. Endocr Connect 10(10):1253\u20131265","journal-title":"Endocr Connect"},{"issue":"8","key":"10736_CR236","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1016\/j.ophtha.2018.01.023","volume":"125","author":"Z Li","year":"2018","unstructured":"Li Z, He Y, Keel S, Meng W, Chang RT, He M (2018) Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 125(8):1199\u20131206","journal-title":"Ophthalmology"},{"issue":"12","key":"10736_CR237","doi-asserted-by":"publisher","first-page":"2509","DOI":"10.2337\/dc18-0147","volume":"41","author":"Z Li","year":"2018","unstructured":"Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, Lee PY, Shaw J, Ting D, Wong TY et al (2018) An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care 41(12):2509\u20132516","journal-title":"Diabetes Care"},{"issue":"12","key":"10736_CR238","doi-asserted-by":"publisher","first-page":"6204","DOI":"10.1364\/BOE.10.006204","volume":"10","author":"F Li","year":"2019","unstructured":"Li F, Chen H, Liu Z, Zhang Xd, Jiang Ms Wu, Kq Zz Zhou (2019) Deep learning-based automated detection of retinal diseases using optical coherence tomography images. Biomed Opt Express 10(12):6204\u20136226","journal-title":"Biomed Opt Express"},{"issue":"6","key":"10736_CR239","first-page":"1012","volume":"12","author":"MX Li","year":"2019","unstructured":"Li MX, Yu SQ, Zhang W, Zhou H, Xu X, Qian TW, Wan YJ (2019) Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images. Int J Ophthalmol 12(6):1012","journal-title":"Int J Ophthalmol"},{"issue":"2","key":"10736_CR240","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1167\/tvst.9.2.61","volume":"9","author":"Q Li","year":"2020","unstructured":"Li Q, Li S, He Z, Guan H, Chen R, Xu Y, Wang T, Qi S, Mei J, Wang W (2020) Deepretina: layer segmentation of retina in OCT images using deep learning. Transl Vis Sci Technol 9(2):61","journal-title":"Transl Vis Sci Technol"},{"issue":"4","key":"10736_CR241","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1364\/BOE.417212","volume":"12","author":"J Li","year":"2021","unstructured":"Li J, Jin P, Zhu J, Zou H, Xu X, Tang M, Zhou M, Gan Y, He J, Ling Y et al (2021) Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images. Biomed Opt Express 12(4):2204\u20132220","journal-title":"Biomed Opt Express"},{"key":"10736_CR242","doi-asserted-by":"publisher","first-page":"102903","DOI":"10.1016\/j.pdpdt.2022.102903","volume":"39","author":"Z Li","year":"2022","unstructured":"Li Z, Deng X, Lu T, Zhou L, Xiao J, Lan Y, Jin C (2022) Hyperreflective material serves as a potential biomarker of dyslipidemia in diabetic macular edema. Photodiagn Photodyn Ther 39:102903","journal-title":"Photodiagn Photodyn Ther"},{"issue":"112","key":"10736_CR243","first-page":"316","volume":"206","author":"J Li","year":"2023","unstructured":"Li J, Gao G, Liu Y, Yang L (2023) MAGF-Net: a multiscale attention-guided fusion network for retinal vessel segmentation. Measurement 206(112):316","journal-title":"Measurement"},{"issue":"3","key":"10736_CR244","first-page":"345","volume":"12","author":"J Li","year":"2024","unstructured":"Li J et al (2024) Patient-specific multi-modal AI approach for predicting disease trajectory in diabetic retinopathy. Ophthalmol Pers Med 12(3):345\u2013358","journal-title":"Ophthalmol Pers Med"},{"key":"10736_CR245","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.optlastec.2013.10.018","volume":"58","author":"M Liao","year":"2014","unstructured":"Liao M, Zhao Yq, Wang Xh, Dai Ps (2014) Retinal vessel enhancement based on multi-scale top-hat transformation and histogram fitting stretching. Opt Laser Technol 58:56\u201362","journal-title":"Opt Laser Technol"},{"key":"10736_CR246","doi-asserted-by":"crossref","unstructured":"Li L, Fang F, Feng X, Zhuang P, Huang H, Liu P, Liu L, Xu AZ, Qi LS, Cong L et al (2022a) Single-cell transcriptome analysis of regenerating RGCs reveals potent glaucoma neural repair genes. Neuron","DOI":"10.1016\/j.neuron.2022.06.022"},{"key":"10736_CR247","doi-asserted-by":"crossref","unstructured":"Lim G, Cheng Y, Hsu W, Lee ML (2015) Integrated optic disc and cup segmentation with deep learning. In: 2015 IEEE 27th international conference on tools with artificial intelligence (ICTAI). IEEE, pp 162\u2013169","DOI":"10.1109\/ICTAI.2015.36"},{"key":"10736_CR248","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40662-020-00182-7","volume":"7","author":"G Lim","year":"2020","unstructured":"Lim G, Bellemo V, Xie Y, Lee XQ, Yip MY, Ting DS (2020) Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review. Eye Vis 7:1\u201313","journal-title":"Eye Vis"},{"issue":"11","key":"10736_CR249","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","volume":"35","author":"P Liskowski","year":"2016","unstructured":"Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369\u20132380","journal-title":"IEEE Trans Med Imaging"},{"key":"10736_CR250","doi-asserted-by":"crossref","unstructured":"Liu P, Kong B, Li Z, Zhang S, Fang R (2019a) Cfea: collaborative feature ensembling adaptation for domain adaptation in unsupervised optic disc and cup segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 521\u2013529","DOI":"10.1007\/978-3-030-32254-0_58"},{"issue":"103","key":"10736_CR251","first-page":"485","volume":"115","author":"S Liu","year":"2019","unstructured":"Liu S, Hong J, Lu X, Jia X, Lin Z, Zhou Y, Liu Y, Zhang H (2019b) Joint optic disc and cup segmentation using semi-supervised conditional GANs. Comput Biol Med 115(103):485","journal-title":"Comput Biol Med"},{"issue":"3","key":"10736_CR253","doi-asserted-by":"publisher","first-page":"60","DOI":"10.3390\/a13030060","volume":"13","author":"W Liu","year":"2020","unstructured":"Liu W, Sun Y, Ji Q (2020) MDAN-UNet: multi-scale and dual attention enhanced nested u-net architecture for segmentation of optical coherence tomography images. Algorithms 13(3):60","journal-title":"Algorithms"},{"issue":"1","key":"10736_CR254","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-020-00528-6","volume":"21","author":"B Liu","year":"2021","unstructured":"Liu B, Pan D, Song H (2021) Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network. BMC Med Imaging 21(1):1\u201312","journal-title":"BMC Med Imaging"},{"key":"10736_CR255","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1016\/j.neucom.2020.07.143","volume":"452","author":"X Liu","year":"2021","unstructured":"Liu X, Wang S, Zhang Y, Liu D, Hu W (2021) Automatic fluid segmentation in retinal optical coherence tomography images using attention based deep learning. Neurocomputing 452:576\u2013591","journal-title":"Neurocomputing"},{"key":"10736_CR256","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.neucom.2021.10.076","volume":"469","author":"P Liu","year":"2022","unstructured":"Liu P, Tran CT, Kong B, Fang R (2022) CADA: multi-scale collaborative adversarial domain adaptation for unsupervised optic disc and cup segmentation. Neurocomputing 469:209\u2013220","journal-title":"Neurocomputing"},{"issue":"24","key":"10736_CR257","doi-asserted-by":"publisher","first-page":"245012","DOI":"10.1088\/1361-6560\/aca376","volume":"67","author":"X Liu","year":"2022","unstructured":"Liu X, Zhou K, Yao J, Wang M, Zhang Y (2022) Contrastive uncertainty based biomarkers detection in retinal optical coherence tomography images. Phys Med Biol 67(24):245012","journal-title":"Phys Med Biol"},{"issue":"104","key":"10736_CR258","first-page":"087","volume":"79","author":"Y Liu","year":"2023","unstructured":"Liu Y, Shen J, Yang L, Bian G, Yu H (2023) ResDO-UNet: a deep residual network for accurate retinal vessel segmentation from fundus images. Biomed Signal Process Control 79(104):087","journal-title":"Biomed Signal Process Control"},{"issue":"106","key":"10736_CR259","first-page":"467","volume":"153","author":"X Liu","year":"2023","unstructured":"Liu X, Liu Q, Zhang Y, Wang M, Tang J (2023) TSSK-Net: Weakly supervised biomarker localization and segmentation with image-level annotation in retinal OCT images. Comput Biol Med 153(106):467","journal-title":"Comput Biol Med"},{"issue":"106","key":"10736_CR260","first-page":"341","volume":"152","author":"Y Liu","year":"2023","unstructured":"Liu Y, Shen J, Yang L, Yu H, Bian G (2023) Wave-Net: a lightweight deep network for retinal vessel segmentation from fundus images. Comput Biol Med 152(106):341","journal-title":"Comput Biol Med"},{"key":"10736_CR261","doi-asserted-by":"crossref","unstructured":"Liu X, Bai Y, Jiang M (2021b) One-stage attention-based network for image classification and segmentation on optical coherence tomography image. In: 2021 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 3025\u20133029","DOI":"10.1109\/SMC52423.2021.9658976"},{"issue":"3","key":"10736_CR262","first-page":"033012","volume":"28","author":"Z Lu","year":"2019","unstructured":"Lu Z, Chen D, Xue D, Zhang S (2019) Weakly supervised semantic segmentation for optic disc of fundus image. J Electron Imaging 28(3):033012","journal-title":"J Electron Imaging"},{"issue":"104","key":"10736_CR263","first-page":"365","volume":"81","author":"Z Lu","year":"2023","unstructured":"Lu Z, Miao J, Dong J, Zhu S, Wang X, Feng J (2023) Automatic classification of retinal diseases with transfer learning-based lightweight convolutional neural network. Biomed Signal Process Control 81(104):365","journal-title":"Biomed Signal Process Control"},{"key":"10736_CR264","doi-asserted-by":"crossref","unstructured":"Madadi Y, Abu-Serhan H, Yousefi S (2022) Domain adaptation-based deep learning models for forecasting and diagnosis of glaucoma disease. TechRxiv","DOI":"10.36227\/techrxiv.21391551.v1"},{"issue":"8","key":"10736_CR265","first-page":"5223","volume":"34","author":"S Madathil","year":"2022","unstructured":"Madathil S, Padannayil SK (2022) MC-DMD: a data-driven method for blood vessel enhancement in retinal images using morphological closing and dynamic mode decomposition. J King Saud Univ Comput Inf Sci 34(8):5223\u20135239","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"7","key":"10736_CR266","doi-asserted-by":"publisher","first-page":"e0219126","DOI":"10.1371\/journal.pone.0219126","volume":"14","author":"S Maetschke","year":"2019","unstructured":"Maetschke S, Antony B, Ishikawa H, Wollstein G, Schuman J, Garnavi R (2019) A feature agnostic approach for glaucoma detection in OCT volumes. PLoS One 14(7):e0219126. https:\/\/doi.org\/10.1371\/journal.pone.0219126","journal-title":"PLoS One"},{"key":"10736_CR267","doi-asserted-by":"crossref","unstructured":"Mahapatra S, Agrawal S (2021) An optimal statistical feature-based transformation function for enhancement of retinal images using adaptive enhanced leader particle swarm optimization. Int J Imaging Syst Technol","DOI":"10.1002\/ima.22767"},{"issue":"102","key":"10736_CR268","first-page":"058","volume":"98","author":"MT Mahmood","year":"2022","unstructured":"Mahmood MT, Lee IH (2022) Optic disc localization in fundus images through accumulated directional and radial blur analysis. Comput Med Imaging Graph 98(102):058","journal-title":"Comput Med Imaging Graph"},{"key":"10736_CR269","doi-asserted-by":"crossref","unstructured":"Mai S, Li Q, Zhao Q, Gao M (2021) Few-shot transfer learning for hereditary retinal diseases recognition. In: Medical image computing and computer assisted intervention\u2013MICCAI 2021: 24th international conference, Strasbourg, Proceedings, Part VIII 24. Springer, pp 97\u2013107","DOI":"10.1007\/978-3-030-87237-3_10"},{"issue":"17","key":"10736_CR270","doi-asserted-by":"publisher","first-page":"7821","DOI":"10.1364\/OE.14.007821","volume":"14","author":"S Makita","year":"2006","unstructured":"Makita S, Hong Y, Yamanari M, Yatagai T, Yasuno Y (2006) Optical coherence angiography. Opt Express 14(17):7821\u20137840","journal-title":"Opt Express"},{"key":"10736_CR271","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.neucom.2022.10.001","volume":"515","author":"N Man","year":"2023","unstructured":"Man N, Guo S, Yiu K, Leung C (2023) Multi-layer segmentation of retina OCT images via advanced u-net architecture. Neurocomputing 515:185\u2013200","journal-title":"Neurocomputing"},{"issue":"1","key":"10736_CR272","first-page":"44","volume":"3","author":"S Manoj","year":"2013","unstructured":"Manoj S, Muralidharan SP, Sandeep M (2013) Neural network based classifier for retinal blood vessel segmentation. Int J Recent Trends Electr Electron Eng 3(1):44\u201353","journal-title":"Int J Recent Trends Electr Electron Eng"},{"issue":"6","key":"10736_CR273","doi-asserted-by":"publisher","first-page":"455","DOI":"10.4103\/0301-4738.86312","volume":"59","author":"T Mansoori","year":"2011","unstructured":"Mansoori T, Viswanath K, Balakrishna N (2011) Ability of spectral domain optical coherence tomography peripapillary retinal nerve fiber layer thickness measurements to identify early glaucoma. Indian J Ophthalmol 59(6):455","journal-title":"Indian J Ophthalmol"},{"issue":"4","key":"10736_CR274","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1167\/tvst.10.4.17","volume":"10","author":"I Mantel","year":"2021","unstructured":"Mantel I, Mosinska A, Bergin C, Polito MS, Guidotti J, Apostolopoulos S, Ciller C, De Zanet S (2021) Automated quantification of pathological fluids in neovascular age-related macular degeneration, and its repeatability using deep learning. Transl Vis Sci Technol 10(4):17","journal-title":"Transl Vis Sci Technol"},{"key":"10736_CR275","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-57196-y","author":"EB Mariottoni","year":"2020","unstructured":"Mariottoni EB, Jammal AA, Urata CN, Berchuck SI, Thompson AC, Estrela T, Medeiros FA (2020) Quantification of retinal nerve fibre layer thickness on optical coherence tomography with a deep learning segmentation-free approach. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-019-57196-y","journal-title":"Sci Rep"},{"key":"10736_CR276","doi-asserted-by":"crossref","unstructured":"Matta S, Lamard M, Conze PH, Le Guilcher A, Ricquebourg V, Benyoussef AA, Massin P, Rottier JB, Cochener B, Quellec G (2023) Meta learning for anomaly detection in fundus photographs. In: Meta-learning with medical imaging and health informatics applications. Elsevier, pp 301\u2013329","DOI":"10.1016\/B978-0-32-399851-2.00025-9"},{"issue":"2","key":"10736_CR277","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1097\/APO.0000000000000496","volume":"11","author":"MM Mauschitz","year":"2022","unstructured":"Mauschitz MM, Finger RP (2022) Age-related macular degeneration and cardiovascular diseases: revisiting the common soil theory. Asia-Pac J Ophthalmol 11(2):94\u201399","journal-title":"Asia-Pac J Ophthalmol"},{"issue":"1","key":"10736_CR278","doi-asserted-by":"publisher","first-page":"2757","DOI":"10.1038\/s41598-022-06821-4","volume":"12","author":"MM Mauschitz","year":"2022","unstructured":"Mauschitz MM, Lohner V, Koch A, St\u00f6cker T, Reuter M, Holz FG, Finger RP, Breteler MM (2022) Retinal layer assessments as potential biomarkers for brain atrophy in the Rhineland study. Sci Rep 12(1):2757","journal-title":"Sci Rep"},{"key":"10736_CR279","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.ajo.2021.10.008","volume":"236","author":"MM Mauschitz","year":"2022","unstructured":"Mauschitz MM, Schmitz MT, Verzijden T, Schmid M, Thee EF, Colijn JM, Delcourt C, Cougnard-Gr\u00e9goire A, Merle BM, Korobelnik JF et al (2022) Physical activity, incidence, and progression of age-related macular degeneration: a multicohort study. Am J Ophthalmol 236:99\u2013106","journal-title":"Am J Ophthalmol"},{"issue":"5","key":"10736_CR280","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1364\/boe.1.001358","volume":"1","author":"MA Mayer","year":"2010","unstructured":"Mayer MA, Hornegger J, Mardin CY, Tornow RP (2010) Retinal nerve fiber layer segmentation on FD-OCT scans of normal subjects and glaucoma patients. Biomed Opt Express 1(5):1358. https:\/\/doi.org\/10.1364\/boe.1.001358","journal-title":"Biomed Opt Express"},{"key":"10736_CR281","unstructured":"Mayo Clinic, Glaucoma. https:\/\/www.mayoclinic.org\/diseases-conditions\/glaucoma\/symptoms-causes\/syc-20372839. Accessed 12 Dec 2022"},{"issue":"9","key":"10736_CR282","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1001\/archophthalmol.2012.827","volume":"130","author":"FA Medeiros","year":"2012","unstructured":"Medeiros FA, Lisboa PR, Weinreb RN, Girkin CA, Liebmann JM, Zangwill LM (2012) A combined index of structure and function for staging glaucomatous damage. Arch Ophthalmol 130(9):1107\u20131116","journal-title":"Arch Ophthalmol"},{"key":"10736_CR283","doi-asserted-by":"crossref","unstructured":"Melinscak M, Prentasic P, Loncaric S (2015) Retinal vessel segmentation using deep neural networks. In: VISAPP (1), pp 577\u2013582","DOI":"10.5220\/0005313005770582"},{"key":"10736_CR284","doi-asserted-by":"crossref","unstructured":"Meng Q, Shin\u2019ichi S (2020) ADINet: attribute driven incremental network for retinal image classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 4033\u20134042","DOI":"10.1109\/CVPR42600.2020.00409"},{"key":"10736_CR285","unstructured":"Messidor (2017) Messidor: methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology. https:\/\/www.adcis.net\/en\/third-party\/messidor\/"},{"issue":"9","key":"10736_CR286","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1167\/tvst.10.9.22","volume":"10","author":"E Mikula","year":"2021","unstructured":"Mikula E, Holland G, Bradford S, Khazaeinezhad R, Srass H, Suarez C, Jester JV, Juhasz T (2021) Intraocular pressure reduction by femtosecond laser created trabecular channels in perfused human anterior segments. Transl Vis Sci Technol 10(9):22","journal-title":"Transl Vis Sci Technol"},{"issue":"3","key":"10736_CR287","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1167\/tvst.11.3.28","volume":"11","author":"ER Mikula","year":"2022","unstructured":"Mikula ER, Raksi F, Ahmed II, Sharma M, Holland G, Khazaeinezhad R, Bradford S, Jester JV, Juhasz T (2022) Femtosecond laser trabeculotomy in perfused human cadaver anterior segments: a novel, noninvasive approach to glaucoma treatment. Transl Vis Sci Technol 11(3):28","journal-title":"Transl Vis Sci Technol"},{"issue":"1","key":"10736_CR288","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1080\/13816810.2021.1970197","volume":"43","author":"P Milanowski","year":"2022","unstructured":"Milanowski P, Kosior-Jarecka E, Lukasik U, Wrobel-Dudzinska D, Milanowska J, Khor CC, Aung T, Kocki J, Zarnowski T (2022) Associations between opa1, mfn1, and mfn2 polymorphisms and primary open angle glaucoma in polish participants of european ancestry. Ophthalmic Genet 43(1):42\u201347","journal-title":"Ophthalmic Genet"},{"issue":"1","key":"10736_CR289","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-66355-5","volume":"10","author":"Z Mishra","year":"2020","unstructured":"Mishra Z, Ganegoda A, Selicha J, Wang Z, Sadda SR, Hu Z (2020) Automated retinal layer segmentation using graph-based algorithm incorporating deep-learning-derived information. Sci Rep 10(1):1\u20138","journal-title":"Sci Rep"},{"issue":"4","key":"10736_CR290","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1109\/TAI.2021.3135797","volume":"3","author":"SS Mishra","year":"2021","unstructured":"Mishra SS, Mandal B, Puhan NB (2021) Perturbed composite attention model for macular optical coherence tomography image classification. IEEE Trans Artif Intell 3(4):625\u2013635","journal-title":"IEEE Trans Artif Intell"},{"key":"10736_CR291","doi-asserted-by":"crossref","unstructured":"Mistry S, Tonyushkina KN, Benavides VC, Choudhary A, Huerta-Saenz L, Patel NS, Mahmud FH, Libman I, Sperling MA (2022) A centennial review of discoveries and advances in diabetes: children and youth. Pediatric Diabetes","DOI":"10.1111\/pedi.13392"},{"issue":"3","key":"10736_CR292","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10384-022-00909-0","volume":"66","author":"S Mochida","year":"2022","unstructured":"Mochida S, Yoshida T, Nomura T, Hatake R, Ohno-Matsui K (2022) Association between peripheral visual field defects and focal lamina cribrosa defects in highly myopic eyes. Jpn J Ophthalmol 66(3):285\u2013295","journal-title":"Jpn J Ophthalmol"},{"issue":"101","key":"10736_CR293","first-page":"454","volume":"53","author":"NA Mohamed","year":"2019","unstructured":"Mohamed NA, Zulkifley MA, Zaki WMDW, Hussain A (2019) An automated glaucoma screening system using cup-to-disc ratio via simple linear iterative clustering superpixel approach. Biomed Signal Process Control 53(101):454","journal-title":"Biomed Signal Process Control"},{"key":"10736_CR294","doi-asserted-by":"crossref","unstructured":"Moin K, Shrivastava M, Mishra A, Jena L, Nayak S (2023) Diabetic retinopathy detection using CNN model. In: Ambient intelligence in health care. Springer, pp 133\u2013143","DOI":"10.1007\/978-981-19-6068-0_13"},{"key":"10736_CR295","doi-asserted-by":"publisher","first-page":"106512","DOI":"10.1016\/j.compbiomed.2022.106512","volume":"154","author":"M Moradi","year":"2023","unstructured":"Moradi M, Chen Y, Du X, Seddon JM (2023) Deep ensemble learning for automated non-advanced AMD classification using optimized retinal layer segmentation and SD-OCT scans. Comput Biol Med 154:106512","journal-title":"Comput Biol Med"},{"key":"10736_CR296","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2019.01117","author":"S Motamedi","year":"2019","unstructured":"Motamedi S, Gawlik K, Ayadi N, Zimmermann HG, Asseyer S, Bereuter C, Mikolajczak J, Paul F, Kadas EM, Brandt AU (2019) Normative data and minimally detectable change for inner retinal layer thicknesses using a semi-automated OCT image segmentation pipeline. Front Neurol. https:\/\/doi.org\/10.3389\/fneur.2019.01117","journal-title":"Front Neurol"},{"issue":"4","key":"10736_CR297","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.irbm.2021.06.004","volume":"43","author":"Y Mrad","year":"2022","unstructured":"Mrad Y, Elloumi Y, Akil M, Bedoui M (2022) A fast and accurate method for glaucoma screening from smartphone-captured fundus images. IRBM 43(4):279\u2013289","journal-title":"IRBM"},{"issue":"12","key":"10736_CR298","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1097\/ijg.0000000000000765","volume":"26","author":"H Muhammad","year":"2017","unstructured":"Muhammad H, Fuchs TJ, Cuir ND, Moraes CGD, Blumberg DM, Liebmann JM, Ritch R, Hood DC (2017) Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J Glaucoma 26(12):1086\u20131094. https:\/\/doi.org\/10.1097\/ijg.0000000000000765","journal-title":"J Glaucoma"},{"issue":"6","key":"10736_CR299","doi-asserted-by":"publisher","first-page":"3195","DOI":"10.1364\/BOE.450193","volume":"13","author":"S Mukherjee","year":"2022","unstructured":"Mukherjee S, De Silva T, Grisso P, Wiley H, Tiarnan DK, Thavikulwat AT, Chew E, Cukras C (2022) Retinal layer segmentation in optical coherence tomography (OCT) using a 3d deep-convolutional regression network for patients with age-related macular degeneration. Biomed Opt Express 13(6):3195\u20133210","journal-title":"Biomed Opt Express"},{"issue":"9","key":"10736_CR300","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.3390\/jpm12091384","volume":"12","author":"GZ Munteanu","year":"2022","unstructured":"Munteanu GZ, Munteanu ZVI, Daina CM, Daina LG, Coroi MC, Domnariu C, Badau D, Roiu G (2022) Study to identify and evaluate predictor factors for primary open-angle glaucoma in tertiary prophylactic actions. J Pers Med 12(9):1384","journal-title":"J Pers Med"},{"issue":"9","key":"10736_CR301","first-page":"1895","volume":"59","author":"D Muramatsu","year":"2018","unstructured":"Muramatsu D, Shimura M, Kitano S, Sakamoto T (2018) Survey of TREATment for diabetic macular edema (STREAT-DME) study: results by treatment options from real world data in Japan. Investig Ophthalmol Vis Sci 59(9):1895","journal-title":"Investig Ophthalmol Vis Sci"},{"issue":"3","key":"10736_CR302","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1007\/s00500-022-06752-2","volume":"26","author":"R Murugan","year":"2022","unstructured":"Murugan R, Roy P (2022) Micronet: microaneurysm detection in retinal fundus images using convolutional neural network. Soft Comput 26(3):1057\u20131066","journal-title":"Soft Comput"},{"issue":"111","key":"10736_CR303","first-page":"485","volume":"200","author":"M Murugappan","year":"2022","unstructured":"Murugappan M, Prakash N, Jeya R, Mohanarathinam A, Hemalakshmi G, Mahmud M (2022) A novel few-shot classification framework for diabetic retinopathy detection and grading. Measurement 200(111):485","journal-title":"Measurement"},{"key":"10736_CR304","doi-asserted-by":"publisher","first-page":"105648","DOI":"10.1016\/j.compbiomed.2022.105648","volume":"146","author":"P Muthukannan","year":"2022","unstructured":"Muthukannan P et al (2022) Optimized convolution neural network based multiple eye disease detection. Comput Biol Med 146:105648","journal-title":"Comput Biol Med"},{"issue":"101","key":"10736_CR305","first-page":"643","volume":"77","author":"A Mvoulana","year":"2019","unstructured":"Mvoulana A, Kachouri R, Akil M (2019) Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images. Comput Med Imaging Graph 77(101):643","journal-title":"Comput Med Imaging Graph"},{"issue":"11","key":"10736_CR306","doi-asserted-by":"publisher","first-page":"5724","DOI":"10.1167\/iovs.10-5222","volume":"51","author":"JC Mwanza","year":"2010","unstructured":"Mwanza JC, Chang RT, Budenz DL, Durbin MK, Gendy MG, Shi W, Feuer WJ (2010) Reproducibility of peripapillary retinal nerve fiber layer thickness and optic nerve head parameters measured with cirrus hd-oct in glaucomatous eyes. Investig Ophthalmol Vis Sci 51(11):5724\u20135730","journal-title":"Investig Ophthalmol Vis Sci"},{"key":"10736_CR307","doi-asserted-by":"crossref","unstructured":"Nair AT, Muthuvel K, Haritha K (2022) Effectual evaluation on diabetic retinopathy. In: Information and communication technology for competitive strategies (ICTCS 2020). Springer, pp 559\u2013567","DOI":"10.1007\/978-981-16-0739-4_53"},{"issue":"10","key":"10736_CR308","doi-asserted-by":"publisher","first-page":"4247","DOI":"10.18632\/aging.204074","volume":"14","author":"S Nashine","year":"2022","unstructured":"Nashine S, Cohen P, Wan J, Kenney MC (2022) Effect of humanin G (HNG) on inflammation in age-related macular degeneration (AMD). Aging 14(10):4247","journal-title":"Aging"},{"key":"10736_CR309","doi-asserted-by":"publisher","unstructured":"Naveed M, Ramzan A, Akram MU (2017) Clinical and technical perspective of glaucoma detection using OCT and fundus images: a review. In: 2017 1st international conference on next generation computing applications (NextComp). pp 157-162. https:\/\/doi.org\/10.1109\/NEXTCOMP.2017.8016192","DOI":"10.1109\/NEXTCOMP.2017.8016192"},{"issue":"2","key":"10736_CR310","doi-asserted-by":"publisher","first-page":"434","DOI":"10.3390\/s22020434","volume":"22","author":"M Nawaz","year":"2022","unstructured":"Nawaz M, Nazir T, Javed A, Tariq U, Yong HS, Khan MA, Cha J (2022) An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization. Sensors 22(2):434","journal-title":"Sensors"},{"issue":"2","key":"10736_CR311","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.survophthal.2012.07.004","volume":"58","author":"B Nicholson","year":"2013","unstructured":"Nicholson B, Noble J, Forooghian F, Meyerle C (2013) Central serous chorioretinopathy: update on pathophysiology and treatment. Surv Ophthalmol 58(2):103\u2013126","journal-title":"Surv Ophthalmol"},{"key":"10736_CR312","doi-asserted-by":"publisher","DOI":"10.17485\/ijst\/2015\/v8i24\/80151","author":"R Nithya","year":"2015","unstructured":"Nithya R, Venkateswaran N (2015) Analysis of segmentation algorithms in colour fundus and OCT images for glaucoma detection. Indian J Sci Technol. https:\/\/doi.org\/10.17485\/ijst\/2015\/v8i24\/80151","journal-title":"Indian J Sci Technol."},{"key":"10736_CR313","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.compbiomed.2014.08.028","volume":"54","author":"S Niu","year":"2014","unstructured":"Niu S, Chen Q, de Sisternes L, Rubin DL, Zhang W, Liu Q (2014) Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint. Comput Biol Med 54:116\u2013128. https:\/\/doi.org\/10.1016\/j.compbiomed.2014.08.028","journal-title":"Comput Biol Med"},{"key":"10736_CR314","doi-asserted-by":"publisher","first-page":"710329","DOI":"10.3389\/fmed.2021.710329","volume":"8","author":"R Nuzzi","year":"2021","unstructured":"Nuzzi R, Boscia G, Marolo P, Ricardi F (2021) The impact of artificial intelligence and deep learning in eye diseases: a review. Front Med 8:710329","journal-title":"Front Med"},{"key":"10736_CR316","first-page":"4","volume":"226","author":"K Ohno-Matsui","year":"2021","unstructured":"Ohno-Matsui K et al (2021) International classification and grading system for myopic maculopathy. Am J Ophthalmol 226:4\u201330","journal-title":"Am J Ophthalmol"},{"issue":"3","key":"10736_CR317","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1167\/tvst.8.3.25","volume":"8","author":"G Ometto","year":"2019","unstructured":"Ometto G, Moghul I, Montesano G, Hunter A, Pontikos N, Jones PR, Keane PA, Liu X, Denniston AK, Crabb DP (2019) ReLayer: a free, online tool for extracting retinal thickness from cross-platform OCT images. Transl Vis Sci Technol 8(3):25. https:\/\/doi.org\/10.1167\/tvst.8.3.25","journal-title":"Transl Vis Sci Technol"},{"issue":"2","key":"10736_CR318","doi-asserted-by":"publisher","first-page":"89","DOI":"10.2174\/1574888X15666191218094020","volume":"15","author":"HC O\u2019Neill","year":"2020","unstructured":"O\u2019Neill HC, Limnios IJ, Barnett NL (2020) Advancing a stem cell therapy for age-related macular degeneration. Curr Stem Cell Res Ther 15(2):89\u201397","journal-title":"Curr Stem Cell Res Ther"},{"issue":"1","key":"10736_CR320","first-page":"73","volume":"2","author":"H Oraby","year":"2022","unstructured":"Oraby H, Elshaer S, Rashed L, Eldesoky N (2022) Microrna-499 gene expression in Egyptian type 2 diabetes mellitus patients with and without coronary heart disease. Azhar Int J Pharm Med Sci 2(1):73\u201381","journal-title":"Azhar Int J Pharm Med Sci"},{"key":"10736_CR321","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1111\/pedi.12426","volume":"17","author":"D Pacaud","year":"2016","unstructured":"Pacaud D, Schwandt A, de Beaufort C, Casteels K, Beltrand J, Birkebaek NH, Campagnoli M, Bratina N, Limbert C, O\u2019Riordan MPS et al (2016) A description of clinician reported diagnosis of type 2 diabetes and other non-type 1 diabetes included in a large international multicentered pediatric diabetes registry (sweet). Pediatr Diabetes 17:24\u201331","journal-title":"Pediatr Diabetes"},{"issue":"3","key":"10736_CR322","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.bbe.2017.05.008","volume":"37","author":"R Panda","year":"2017","unstructured":"Panda R, Puhan N, Panda G (2017) Robust and accurate optic disk localization using vessel symmetry line measure in fundus images. Biocybern Biomed Eng 37(3):466\u2013476","journal-title":"Biocybern Biomed Eng"},{"issue":"4","key":"10736_CR324","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/j.ophtha.2012.09.051","volume":"120","author":"HYL Park","year":"2013","unstructured":"Park HYL, Park CK (2013) Diagnostic capability of lamina cribrosa thickness by enhanced depth imaging and factors affecting thickness in patients with glaucoma. Ophthalmology 120(4):745\u2013752. https:\/\/doi.org\/10.1016\/j.ophtha.2012.09.051","journal-title":"Ophthalmology"},{"issue":"11","key":"10736_CR325","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.1097\/IAE.0000000000001077","volume":"36","author":"JJ Park","year":"2016","unstructured":"Park JJ, Soetikno BT, Fawzi AA (2016) Characterization of the middle capillary plexus using optical coherence tomography angiography in healthy and diabetic eyes. Retina 36(11):2039","journal-title":"Retina"},{"key":"10736_CR326","doi-asserted-by":"publisher","first-page":"146308","DOI":"10.1109\/ACCESS.2020.3015108","volume":"8","author":"KB Park","year":"2020","unstructured":"Park KB, Choi SH, Lee JY (2020) M-GAN: Retinal blood vessel segmentation by balancing losses through stacked deep fully convolutional networks. IEEE Access 8:146308\u2013146322","journal-title":"IEEE Access"},{"issue":"106","key":"10736_CR327","first-page":"174","volume":"150","author":"E Parra-Mora","year":"2022","unstructured":"Parra-Mora E, da Silva Cruz LA (2022) LOCTseg: a lightweight fully convolutional network for end-to-end optical coherence tomography segmentation. Comput Biol Med 150(106):174","journal-title":"Comput Biol Med"},{"key":"10736_CR328","doi-asserted-by":"crossref","unstructured":"Parthiban K, Kamarasan M (2022) Efficientnet with optimal wavelet neural network for DR detection and grading. In: 2022 4th international conference on smart systems and inventive technology (ICSSIT). IEEE, pp 1081\u20131086","DOI":"10.1109\/ICSSIT53264.2022.9716528"},{"issue":"5","key":"10736_CR329","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1136\/bjophthalmol-2011-300539","volume":"96","author":"D Pascolini","year":"2012","unstructured":"Pascolini D, Mariotti S (2012) Global estimates of visual impairment: 2010. Br J Ophthalmol 96(5):614\u20138. https:\/\/doi.org\/10.1136\/bjophthalmol-2011-300539","journal-title":"Br J Ophthalmol"},{"issue":"1","key":"10736_CR330","doi-asserted-by":"publisher","first-page":"3","DOI":"10.23736\/S0026-4806.18.05589-1","volume":"110","author":"E Pasini","year":"2019","unstructured":"Pasini E, Corsetti G, Assanelli D, Testa C, Romano C, Dioguardi FS, Aquilani R (2019) Effects of chronic exercise on gut microbiota and intestinal barrier in human with type 2 diabetes. Minerva Med 110(1):3\u201311","journal-title":"Minerva Med"},{"key":"10736_CR331","doi-asserted-by":"publisher","first-page":"100178","DOI":"10.1016\/j.mtnano.2022.100178","volume":"18","author":"KD Patel","year":"2022","unstructured":"Patel KD, Silva LB, Park Y, Shakouri T, Keskin-Erdogan Z, Sawadkar P, Cho KJ, Knowles JC, Chau DY, Kim HW (2022) Recent advances in drug delivery systems for glaucoma treatment. Mater Today Nano 18:100178","journal-title":"Mater Today Nano"},{"key":"10736_CR332","doi-asserted-by":"crossref","unstructured":"Pavithra K, Kumar P, Geetha M, Bhandary SV (2023) Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: a review. Biocybern Biomed Eng","DOI":"10.1016\/j.bbe.2022.12.005"},{"issue":"4","key":"10736_CR333","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1016\/j.survophthal.2019.02.003","volume":"64","author":"E Pead","year":"2019","unstructured":"Pead E, Megaw R, Cameron J, Fleming A, Dhillon B, Trucco E, MacGillivray T (2019) Automated detection of age-related macular degeneration in color fundus photography: a systematic review. Sur Ophthalmol 64(4):498\u2013511","journal-title":"Sur Ophthalmol"},{"issue":"2","key":"10736_CR334","doi-asserted-by":"publisher","first-page":"e663","DOI":"10.1210\/clinem\/dgab669","volume":"107","author":"A Peled","year":"2022","unstructured":"Peled A, Raz I, Zucker I, Derazne E, Megreli J, Pinhas-Hamiel O, Einan-Lifshitz A, Morad Y, Pras E, Lutski M et al (2022) Myopia and early-onset type 2 diabetes: a nationwide cohort study. J Clin Endocrinol Metab 107(2):e663\u2013e671","journal-title":"J Clin Endocrinol Metab"},{"issue":"9","key":"10736_CR335","doi-asserted-by":"publisher","first-page":"555","DOI":"10.33192\/Smj.2022.66","volume":"74","author":"S Petchyim","year":"2022","unstructured":"Petchyim S, Subhadhirasakul S, Sakiyalak D, Vessadapan P, Ruangvaravate N (2022) Clinical characteristics and outcome of bleb-related infection in glaucoma patients. Siriraj Med J 74(9):555\u2013561","journal-title":"Siriraj Med J"},{"issue":"1","key":"10736_CR336","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1038\/s41598-023-27616-1","volume":"13","author":"D Philippi","year":"2023","unstructured":"Philippi D, Rothaus K, Castelli M (2023) A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images. Sci Rep 13(1):517","journal-title":"Sci Rep"},{"issue":"2","key":"10736_CR338","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1097\/ICU.0000000000000735","volume":"32","author":"AJ Prager","year":"2021","unstructured":"Prager AJ, Kang JM, Tanna AP (2021) Advances in perimetry for glaucoma. Curr Opin Ophthalmol 32(2):92\u201397","journal-title":"Curr Opin Ophthalmol"},{"issue":"1","key":"10736_CR339","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12902-021-00718-5","volume":"21","author":"G Prashanth","year":"2021","unstructured":"Prashanth G, Vastrad B, Tengli A, Vastrad C, Kotturshetti I (2021) Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules. BMC Endocr Disord 21(1):1\u201348","journal-title":"BMC Endocr Disord"},{"issue":"1","key":"10736_CR340","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-13291-1","volume":"12","author":"M Pujar","year":"2022","unstructured":"Pujar M, Vastrad B, Kavatagimath S, Vastrad C, Kotturshetti S (2022) Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis. Sci Rep 12(1):1\u201327","journal-title":"Sci Rep"},{"issue":"1","key":"10736_CR341","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12886-022-02296-z","volume":"22","author":"X Qin","year":"2022","unstructured":"Qin X, Zou H (2022) The role of lipopolysaccharides in diabetic retinopathy. BMC Ophthalmol 22(1):1\u201314","journal-title":"BMC Ophthalmol"},{"key":"10736_CR342","doi-asserted-by":"crossref","unstructured":"Qomariah DUN, Tjandrasa H, Fatichah C (2019) Classification of diabetic retinopathy and normal retinal images using CNN and SVM. In: 2019 12th international conference on information & communication technology and system (ICTS). IEEE, pp 152\u2013157","DOI":"10.1109\/ICTS.2019.8850940"},{"issue":"3","key":"10736_CR343","first-page":"359","volume":"14","author":"D Qomariah","year":"2021","unstructured":"Qomariah D, Nopember I, Tjandrasa H, Fatichah C (2021) Segmentation of microaneurysms for early detection of diabetic retinopathy using MResUNet. Int J Intell Eng Syst 14(3):359\u2013373","journal-title":"Int J Intell Eng Syst"},{"key":"10736_CR344","doi-asserted-by":"publisher","first-page":"150530","DOI":"10.1109\/ACCESS.2019.2947484","volume":"7","author":"S Qummar","year":"2019","unstructured":"Qummar S, Khan FG, Shah S, Khan A, Shamshirband S, Rehman ZU, Khan IA, Jadoon W (2019) A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access 7:150530\u2013150539","journal-title":"IEEE Access"},{"issue":"1","key":"10736_CR345","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.cviu.2011.09.001","volume":"116","author":"RJ Qureshi","year":"2012","unstructured":"Qureshi RJ, Kovacs L, Harangi B, Nagy B, Peto T, Hajdu A (2012) Combining algorithms for automatic detection of optic disc and macula in fundus images. Comput Vis Image Underst 116(1):138\u2013145","journal-title":"Comput Vis Image Underst"},{"key":"10736_CR346","doi-asserted-by":"crossref","unstructured":"Qu Z, Zhuo L, Cao J, Li X, Yin H, Wang Z (2023) Tp-net: two-path network for retinal vessel segmentation. IEEE J Biomed Health Inform","DOI":"10.1109\/JBHI.2023.3237704"},{"key":"10736_CR347","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.ins.2018.01.051","volume":"441","author":"U Raghavendra","year":"2018","unstructured":"Raghavendra U, Fujita H, Bhandary SV, Gudigar A, Tan JH, Acharya UR (2018) Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inf Sci 441:41\u201349","journal-title":"Inf Sci"},{"issue":"105","key":"10736_CR348","first-page":"342","volume":"29","author":"H Raja","year":"2020","unstructured":"Raja H, Akram MU, Khawaja SG, Arslan M, Ramzan A, Nazir N (2020) Data on OCT and fundus images for the detection of glaucoma. Data Brief 29(105):342","journal-title":"Data Brief"},{"issue":"6","key":"10736_CR349","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1007\/s10278-020-00383-5","volume":"33","author":"H Raja","year":"2020","unstructured":"Raja H, Akram MU, Shaukat A, Khan SA, Alghamdi N, Khawaja SG, Nazir N (2020) Extraction of retinal layers through convolution neural network (CNN) in an oct image for glaucoma diagnosis. J Digit Imaging 33(6):1428\u20131442","journal-title":"J Digit Imaging"},{"issue":"7","key":"10736_CR350","doi-asserted-by":"publisher","first-page":"2140","DOI":"10.1109\/TBME.2020.3030085","volume":"68","author":"H Raja","year":"2020","unstructured":"Raja H, Hassan T, Akram MU, Werghi N (2020) Clinically verified hybrid deep learning system for retinal ganglion cells aware grading of glaucomatous progression. IEEE Trans Biomed Eng 68(7):2140\u20132151","journal-title":"IEEE Trans Biomed Eng"},{"key":"10736_CR351","doi-asserted-by":"crossref","unstructured":"Raja H, Hassan T, Akram MU, Werghi N (2020c) Clinically verified hybrid deep learning system for retinal ganglion cells aware grading of glaucomatous progression. IEEE Trans Biomed Eng","DOI":"10.1109\/TBME.2020.3030085"},{"key":"10736_CR352","doi-asserted-by":"crossref","unstructured":"Raja H, Akram MU, Hassan T, Ramzan A, Aziz A, Raja H (2022) Glaucoma detection using optical coherence tomography images: a systematic review of clinical and automated studies. IETE J Res 1\u201321","DOI":"10.1080\/03772063.2022.2043783"},{"key":"10736_CR353","doi-asserted-by":"crossref","unstructured":"Rakhlin A (2018) Diabetic retinopathy detection through integration of deep learning classification framework. BioRxiv 225508","DOI":"10.1101\/225508"},{"issue":"1","key":"10736_CR354","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.compmedimag.2013.10.007","volume":"38","author":"SA Ramakanth","year":"2014","unstructured":"Ramakanth SA, Babu RV (2014) Approximate nearest neighbour field based optic disk detection. Comput Med Imaging Graph 38(1):49\u201356","journal-title":"Comput Med Imaging Graph"},{"issue":"5","key":"10736_CR355","first-page":"989","volume":"65","author":"A Rashno","year":"2017","unstructured":"Rashno A, Koozekanani DD, Drayna PM, Nazari B, Sadri S, Rabbani H, Parhi KK (2017) Fully automated segmentation of fluid\/cyst regions in optical coherence tomography images with diabetic macular edema using neutrosophic sets and graph algorithms. IEEE Trans Biomed Eng 65(5):989\u20131001","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"10736_CR356","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1109\/TMI.2017.2780115","volume":"37","author":"R Rasti","year":"2017","unstructured":"Rasti R, Rabbani H, Mehridehnavi A, Hajizadeh F (2017) Macular OCT classification using a multi-scale convolutional neural network ensemble. IEEE Trans Med Imaging 37(4):1024\u20131034","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"10736_CR357","doi-asserted-by":"publisher","first-page":"035005","DOI":"10.1117\/1.JBO.23.3.035005","volume":"23","author":"R Rasti","year":"2018","unstructured":"Rasti R, Mehridehnavi A, Rabbani H, Hajizadeh F (2018) Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier. J Biomed Opt 23(3):035005","journal-title":"J Biomed Opt"},{"key":"10736_CR358","first-page":"4360","volume":"25","author":"S Rathore","year":"2021","unstructured":"Rathore S, Aswal A, Saranya P (2021) Bright lesion detection in retinal fundus images for diabetic retinopathy detection using machine learning approach. Ann Rom Soc Cell Biol 25:4360\u20134367","journal-title":"Ann Rom Soc Cell Biol"},{"issue":"4","key":"10736_CR359","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1166\/jmihi.2014.1289","volume":"4","author":"C Ravichandran","year":"2014","unstructured":"Ravichandran C, Raja JB (2014) A fast enhancement\/thresholding based blood vessel segmentation for retinal image using contrast limited adaptive histogram equalization. J Med Imaging Health Inf 4(4):567\u2013575","journal-title":"J Med Imaging Health Inf"},{"key":"10736_CR360","unstructured":"REFUGE (2020) Refuge: retinal fundus glaucoma challenge. https:\/\/refuge.grand-challenge.org\/"},{"key":"10736_CR361","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.eswa.2018.12.008","volume":"120","author":"ZU Rehman","year":"2019","unstructured":"Rehman ZU, Naqvi SS, Khan TM, Arsalan M, Khan MA, Khalil M (2019) Multi-parametric optic disc segmentation using superpixel based feature classification. Expert Syst Appl 120:461\u2013473","journal-title":"Expert Syst Appl"},{"key":"10736_CR362","unstructured":"RIGA (2018) Retinal fundus images for glaucoma analysis: RIGA dataset. https:\/\/deepblue.lib.umich.edu\/data\/concern\/data_sets\/3b591905z"},{"key":"10736_CR363","unstructured":"RIMONE (2011) Rimone database. https:\/\/medimrg.webs.ull.es\/research\/downloads\/"},{"key":"10736_CR364","unstructured":"RIONS-DB (2009) DRIONS-DB: digital retinal images for optic nerve segmentation database. http:\/\/www.ia.uned.es\/~ejcarmona\/DRIONS-DB.html"},{"issue":"1","key":"10736_CR365","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-07190-8","volume":"12","author":"JS Ro","year":"2022","unstructured":"Ro JS, Moon JY, Park TK, Lee SH (2022) Association between chronic kidney disease and open-angle glaucoma in South Korea: a 12-year nationwide retrospective cohort study. Sci Rep 12(1):1\u20139","journal-title":"Sci Rep"},{"issue":"2","key":"10736_CR366","first-page":"361","volume":"14","author":"N Robert","year":"1995","unstructured":"Robert N (1995) Diabetic retinopathy. Robert NFrank 14(2):361\u2013392","journal-title":"Robert NFrank"},{"key":"10736_CR367","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/s40135-017-0131-6","volume":"5","author":"L Roisman","year":"2017","unstructured":"Roisman L, Goldhardt R (2017) Oct angiography: an upcoming non-invasive tool for diagnosis of age-related macular degeneration. Curr Ophthalmol Rep 5:136\u2013140","journal-title":"Curr Ophthalmol Rep"},{"key":"10736_CR368","doi-asserted-by":"crossref","unstructured":"Roshini R, Alex JSR (2022) Automatic segmentation of optic cup and optic disc using multiresunet for glaucoma classification from fundus image. In: Intelligent vision in healthcare. Springer, pp 33\u201344","DOI":"10.1007\/978-981-16-7771-7_4"},{"issue":"8","key":"10736_CR369","doi-asserted-by":"publisher","first-page":"3627","DOI":"10.1364\/BOE.8.003627","volume":"8","author":"AG Roy","year":"2017","unstructured":"Roy AG, Conjeti S, Karri SPK, Sheet D, Katouzian A, Wachinger C, Navab N (2017) ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express 8(8):3627\u20133642","journal-title":"Biomed Opt Express"},{"key":"10736_CR370","doi-asserted-by":"publisher","unstructured":"Roychowdhury S, Koozekanani DD, Parhi KK (2013) Automated denoising and segmentation of optical coherence tomography images. In: 2013 Asilomar conference on signals, systems and computers. IEEE. https:\/\/doi.org\/10.1109\/acssc.2013.6810272","DOI":"10.1109\/acssc.2013.6810272"},{"issue":"4","key":"10736_CR372","doi-asserted-by":"publisher","first-page":"2904","DOI":"10.3390\/ijerph20042904","volume":"20","author":"E Saeed","year":"2023","unstructured":"Saeed E, Go\u0142aszewska K, Dmuchowska DA, Zalewska R, Konopi\u0144ska J (2023) The preserflo microshunt in the context of minimally invasive glaucoma surgery: a narrative review. Int J Environ Res Public Health 20(4):2904","journal-title":"Int J Environ Res Public Health"},{"issue":"1","key":"10736_CR373","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-47390-3","volume":"9","author":"S Saha","year":"2019","unstructured":"Saha S, Nassisi M, Wang M, Lindenberg S, Sadda S, Hu ZJ et al (2019) Automated detection and classification of early AMD biomarkers using deep learning. Sci Rep 9(1):1\u20139","journal-title":"Sci Rep"},{"issue":"8","key":"10736_CR374","doi-asserted-by":"publisher","first-page":"366","DOI":"10.3390\/bioengineering9080366","volume":"9","author":"GA Saleh","year":"2022","unstructured":"Saleh GA, Batouty NM, Haggag S, Elnakib A, Khalifa F, Taher F, Mohamed MA, Farag R, Sandhu H, Sewelam A et al (2022) The role of medical image modalities and AI in the early detection, diagnosis and grading of retinal diseases: a survey. Bioengineering 9(8):366","journal-title":"Bioengineering"},{"key":"10736_CR375","doi-asserted-by":"crossref","unstructured":"Salehi MA, Mohammadi S, Gouravani M, Rezagholi F, Arevalo JF (2022) Retinal and choroidal changes in AMD: a systematic review and meta-analysis of spectral-domain optical coherence tomography studies. Surv Ophthalmol","DOI":"10.1016\/j.survophthal.2022.07.006"},{"key":"10736_CR376","doi-asserted-by":"publisher","first-page":"151133","DOI":"10.1109\/ACCESS.2020.3015258","volume":"8","author":"R Sarki","year":"2020","unstructured":"Sarki R, Ahmed K, Wang H, Zhang Y (2020) Automatic detection of diabetic eye disease through deep learning using fundus images: a survey. IEEE Access 8:151133\u2013151149","journal-title":"IEEE Access"},{"issue":"1","key":"10736_CR377","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-18875-5","volume":"12","author":"M Sa\u00dfmannshausen","year":"2022","unstructured":"Sa\u00dfmannshausen M, Behning C, Isselmann B, Schmid M, Finger RP, Holz FG, Schmitz-Valckenberg S, Pfau M, Thiele S (2022) Relative ellipsoid zone reflectivity and its association with disease severity in age-related macular degeneration: a macustar study report. Sci Rep 12(1):1\u201312","journal-title":"Sci Rep"},{"issue":"5","key":"10736_CR379","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1016\/j.ophtha.2016.01.034","volume":"123","author":"KB Schaal","year":"2016","unstructured":"Schaal KB, Rosenfeld PJ, Gregori G, Yehoshua Z, Feuer WJ (2016) Anatomic clinical trial endpoints for nonexudative age-related macular degeneration. Ophthalmology 123(5):1060\u20131079","journal-title":"Ophthalmology"},{"issue":"1","key":"10736_CR380","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1097\/IAE.0000000000001938","volume":"39","author":"KB Schaal","year":"2019","unstructured":"Schaal KB, Munk MR, Wyssmueller I, Berger LE, Zinkernagel MS, Wolf S (2019) Vascular abnormalities in diabetic retinopathy assessed with swept-source optical coherence tomography angiography widefield imaging. Retina 39(1):79\u201387","journal-title":"Retina"},{"issue":"4","key":"10736_CR381","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.ophtha.2017.10.031","volume":"125","author":"T Schlegl","year":"2018","unstructured":"Schlegl T, Waldstein SM, Bogunovic H, Endstra\u00dfer F, Sadeghipour A, Philip AM, Podkowinski D, Gerendas BS, Langs G, Schmidt-Erfurth U (2018) Fully automated detection and quantification of macular fluid in oct using deep learning. Ophthalmology 125(4):549\u2013558","journal-title":"Ophthalmology"},{"key":"10736_CR383","doi-asserted-by":"crossref","unstructured":"Schreur V, Larsen MB, Sobrin L, Bhavsar AR, den Hollander AI, Klevering BJ, Hoyng CB, de Jong EK, Grauslund J, Peto T (2022) Imaging diabetic retinal disease: clinical imaging requirements. Acta Ophthalmol","DOI":"10.1111\/aos.15110"},{"key":"10736_CR384","doi-asserted-by":"crossref","unstructured":"Sedai S, Antony B, Rai R, Jones K, Ishikawa H, Schuman J, Gadi W, Garnavi R (2019) Uncertainty guided semi-supervised segmentation of retinal layers in oct images. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 282\u2013290","DOI":"10.1007\/978-3-030-32239-7_32"},{"issue":"6","key":"10736_CR385","doi-asserted-by":"publisher","first-page":"e6768","DOI":"10.1002\/cpe.6768","volume":"34","author":"T Sel\u00e7uk","year":"2022","unstructured":"Sel\u00e7uk T, Beyo\u011flu A, Alkan A (2022) Automatic detection of exudates and hemorrhages in low-contrast color fundus images using multi semantic convolutional neural network. Concurr Comput Pract Exp 34(6):e6768","journal-title":"Concurr Comput Pract Exp"},{"key":"10736_CR386","unstructured":"Seltman W (2021) Age-related macular degeneration overview. https:\/\/www.webmd.com\/eye-health\/macular-degeneration\/age-related-macular-degeneration-overview. Accessed 5 Mar 2022"},{"issue":"4","key":"10736_CR387","doi-asserted-by":"publisher","first-page":"335","DOI":"10.4258\/hir.2018.24.4.335","volume":"24","author":"A Septiarini","year":"2018","unstructured":"Septiarini A, Harjoko A, Pulungan R, Ekantini R (2018) Automated detection of retinal nerve fiber layer by texture-based analysis for glaucoma evaluation. Healthc Inform Res 24(4):335\u2013345","journal-title":"Healthc Inform Res"},{"issue":"4","key":"10736_CR388","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12046-022-01936-w","volume":"47","author":"S Sethuraman","year":"2022","unstructured":"Sethuraman S, Palakuzhiyil Gopi V (2022) Staircase-Net: a deep learning based architecture for retinal blood vessel segmentation. S\u0101dhan\u0101 47(4):1\u20139","journal-title":"S\u0101dhan\u0101"},{"issue":"3","key":"10736_CR389","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1134\/S1054661817030269","volume":"27","author":"A Sevastopolsky","year":"2017","unstructured":"Sevastopolsky A (2017) Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognit Image Anal 27(3):618\u2013624","journal-title":"Pattern Recognit Image Anal"},{"key":"10736_CR390","doi-asserted-by":"publisher","unstructured":"Shah A, Abramoff MD, Wu X (2017) Simultaneous multiple surface segmentation using deep learning. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, pp 3\u201311. https:\/\/doi.org\/10.1007\/978-3-319-67558-9_1","DOI":"10.1007\/978-3-319-67558-9_1"},{"issue":"9","key":"10736_CR391","doi-asserted-by":"publisher","first-page":"4509","DOI":"10.1364\/boe.9.004509","volume":"9","author":"A Shah","year":"2018","unstructured":"Shah A, Zhou L, Abr\u00e1moff MD, Wu X (2018) Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images. Biomed Opt Express 9(9):4509. https:\/\/doi.org\/10.1364\/boe.9.004509","journal-title":"Biomed Opt Express"},{"key":"10736_CR392","doi-asserted-by":"crossref","unstructured":"Shahriari MH, Sabbaghi H, Asadi F, Hosseini A, Khorrami Z (2022) Artificial intelligence in screening, diagnosis, and classification of diabetic macular edema: a systematic review. Surv Ophthalmol","DOI":"10.1016\/j.survophthal.2022.08.004"},{"issue":"4","key":"10736_CR393","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1109\/JBHI.2019.2899403","volume":"23","author":"SM Shankaranarayana","year":"2019","unstructured":"Shankaranarayana SM, Ram K, Mitra K, Sivaprakasam M (2019) Fully convolutional networks for monocular retinal depth estimation and optic disc-cup segmentation. IEEE J Biomed Health Inform 23(4):1417\u20131426","journal-title":"IEEE J Biomed Health Inform"},{"key":"10736_CR394","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.jnutbio.2018.10.003","volume":"63","author":"S Sharma","year":"2019","unstructured":"Sharma S, Tripathi P (2019) Gut microbiome and type 2 diabetes: Where we are and where to go? J Nutr Biochem 63:101\u2013108","journal-title":"J Nutr Biochem"},{"key":"10736_CR395","doi-asserted-by":"crossref","unstructured":"Sharma R, Nappi V, Empeslidis T (2023) The developments in amniotic membrane transplantation in glaucoma and vitreoretinal procedures. Int Ophthalmol 1\u201313","DOI":"10.1007\/s10792-022-02570-5"},{"key":"10736_CR396","doi-asserted-by":"crossref","unstructured":"Sheikh S, Qidwai U (2020) Smartphone-based diabetic retinopathy severity classification using convolution neural networks. In: Proceedings of SAI intelligent systems conference. Springer, 469\u2013481","DOI":"10.1007\/978-3-030-55190-2_35"},{"issue":"102","key":"10736_CR397","first-page":"977","volume":"70","author":"Z Shi","year":"2021","unstructured":"Shi Z, Wang T, Huang Z, Xie F, Liu Z, Wang B, Xu J (2021) Md-net: a multi-scale dense network for retinal vessel segmentation. Biomed Signal Process Control 70(102):977","journal-title":"Biomed Signal Process Control"},{"issue":"7","key":"10736_CR398","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1097\/IJG.0000000000002027","volume":"31","author":"B Siesky","year":"2022","unstructured":"Siesky B, Harris A, Belamkar A, Zukerman R, Horn A, Vercellin AV, Mendoza KA, Sidoti PA, Oddone F (2022) Glaucoma treatment outcomes in open angle glaucoma patients of African descent. J Glaucoma 31(7):479\u2013487","journal-title":"J Glaucoma"},{"key":"10736_CR399","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.cmpb.2015.10.010","volume":"124","author":"A Singh","year":"2016","unstructured":"Singh A, Dutta MK, ParthaSarathi M, Uher V, Burget R (2016) Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Comput Methods Programs Biomed 124:108\u2013120","journal-title":"Comput Methods Programs Biomed"},{"issue":"12","key":"10736_CR400","doi-asserted-by":"publisher","first-page":"107417","DOI":"10.1016\/j.jdiacomp.2019.107417","volume":"33","author":"RP Singh","year":"2019","unstructured":"Singh RP, Elman MJ, Singh SK, Fung AE, Stoilov I (2019) Advances in the treatment of diabetic retinopathy. J Diabetes Complicat 33(12):107417","journal-title":"J Diabetes Complicat"},{"issue":"2","key":"10736_CR401","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/S0161-6420(97)30327-3","volume":"104","author":"AK Sj\u00f8lie","year":"1997","unstructured":"Sj\u00f8lie AK, Stephenson J, Aldington S, Kohner E, Janka H, Stevens L, Fuller J, Karamanos B, Tountas C, Kofinis A et al (1997) Retinopathy and vision loss in insulin-dependent diabetes in Europe: the EURODIAB IDDM complications study. Ophthalmology 104(2):252\u2013260","journal-title":"Ophthalmology"},{"key":"10736_CR402","doi-asserted-by":"crossref","unstructured":"Smitha A, Jidesh P (2022) Detection of retinal disorders from oct images using generative adversarial networks. Multimed Tools Appl 1\u201323","DOI":"10.1007\/s11042-022-12475-1"},{"issue":"3","key":"10736_CR403","first-page":"031205","volume":"18","author":"A Smith","year":"2021","unstructured":"Smith A et al (2021) Deep learning for retinopathy of prematurity diagnosis. J Med Imaging 18(3):031205","journal-title":"J Med Imaging"},{"issue":"3","key":"10736_CR404","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/s10278-018-0126-3","volume":"32","author":"J Son","year":"2019","unstructured":"Son J, Park SJ, Jung KH (2019) Towards accurate segmentation of retinal vessels and the optic disc in fundoscopic images with generative adversarial networks. J Digit Imaging 32(3):499\u2013512","journal-title":"J Digit Imaging"},{"issue":"1","key":"10736_CR405","doi-asserted-by":"publisher","first-page":"13850","DOI":"10.1038\/s41598-022-18061-7","volume":"12","author":"T Son","year":"2022","unstructured":"Son T, Ma J, Toslak D, Rossi A, Kim H, Chan RP, Yao X (2022) Light color efficiency-balanced trans-palpebral illumination for widefield fundus photography of the retina and choroid. Sci Rep 12(1):13850","journal-title":"Sci Rep"},{"key":"10736_CR406","doi-asserted-by":"crossref","unstructured":"Song R, Cao P, Yang J, Zhao D, Zaiane OR (2020) A domain adaptation multi-instance learning for diabetic retinopathy grading on retinal images. In: 2020 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 743\u2013750","DOI":"10.1109\/BIBM49941.2020.9313398"},{"issue":"5","key":"10736_CR407","doi-asserted-by":"publisher","first-page":"e0251591","DOI":"10.1371\/journal.pone.0251591","volume":"16","author":"JA Sousa","year":"2021","unstructured":"Sousa JA, Paiva A, Silva A, Almeida JD, Braz Junior G, Diniz JO, Figueredo WK, Gattass M (2021) Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and dexined. PLoS One 16(5):e0251591","journal-title":"PLoS One"},{"issue":"14","key":"10736_CR408","doi-asserted-by":"publisher","first-page":"4916","DOI":"10.3390\/app10144916","volume":"10","author":"S Sreng","year":"2020","unstructured":"Sreng S, Maneerat N, Hamamoto K, Win KY (2020) Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images. Appl Sci 10(14):4916","journal-title":"Appl Sci"},{"issue":"2","key":"10736_CR409","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1364\/boe.5.000348","volume":"5","author":"PP Srinivasan","year":"2014","unstructured":"Srinivasan PP, Heflin SJ, Izatt JA, Arshavsky VY, Farsiu S (2014) Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology. Biomed Opt Express 5(2):348. https:\/\/doi.org\/10.1364\/boe.5.000348","journal-title":"Biomed Opt Express"},{"issue":"10","key":"10736_CR410","doi-asserted-by":"publisher","first-page":"3568","DOI":"10.1364\/BOE.5.003568","volume":"5","author":"PP Srinivasan","year":"2014","unstructured":"Srinivasan PP, Kim LA, Mettu PS, Cousins SW, Comer GM, Izatt JA, Farsiu S (2014) Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express 5(10):3568\u20133577","journal-title":"Biomed Opt Express"},{"issue":"1","key":"10736_CR411","doi-asserted-by":"publisher","first-page":"11","DOI":"10.4103\/ijo.IJO_1569_22","volume":"71","author":"O Srivastava","year":"2023","unstructured":"Srivastava O, Tennant M, Grewal P, Rubin U, Seamone M (2023) Artificial intelligence and machine learning in ophthalmology: a review. Indian J Ophthalmol 71(1):11\u201317","journal-title":"Indian J Ophthalmol"},{"key":"10736_CR412","unstructured":"STARE (2000) Structured analysis of the retina. http:\/\/cecas.clemson.edu\/~ahoover\/stare\/"},{"issue":"2","key":"10736_CR413","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3390\/hygiene2020007","volume":"2","author":"SA Stoica","year":"2022","unstructured":"Stoica SA, Valentini G, Dolci M, D\u2019Agostino S (2022) Diabetes and non-surgical periodontal therapy: What can we hope for? Hygiene 2(2):85\u201393","journal-title":"Hygiene"},{"issue":"101","key":"10736_CR414","first-page":"742","volume":"64","author":"S Stolte","year":"2020","unstructured":"Stolte S, Fang R (2020) A survey on medical image analysis in diabetic retinopathy. Med Image Anal 64(101):742","journal-title":"Med Image Anal"},{"issue":"8","key":"10736_CR415","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1007\/s00138-016-0781-7","volume":"27","author":"N Strisciuglio","year":"2016","unstructured":"Strisciuglio N, Azzopardi G, Vento M, Petkov N (2016) Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Mach Vis Appl 27(8):1137\u20131149","journal-title":"Mach Vis Appl"},{"issue":"10","key":"10736_CR417","doi-asserted-by":"publisher","first-page":"5518","DOI":"10.1016\/j.sjbs.2021.07.068","volume":"28","author":"M Sufyan","year":"2021","unstructured":"Sufyan M, Ashfaq UA, Ahmad S, Noor F, Saleem MH, Aslam MF, El-Serehy HA, Aslam S (2021) Identifying key genes and screening therapeutic agents associated with diabetes mellitus and HCV-related hepatocellular carcinoma by bioinformatics analysis. Saudi J Biol Sci 28(10):5518\u20135525","journal-title":"Saudi J Biol Sci"},{"key":"10736_CR418","doi-asserted-by":"crossref","unstructured":"Sugmk J, Kiattisin S, Leelasantitham A (2014) Automated classification between age-related macular degeneration and diabetic macular edema in OCT image using image segmentation. In: The 7th 2014 biomedical engineering international conference. IEEE, pp 1\u20134","DOI":"10.1109\/BMEiCON.2014.7017441"},{"key":"10736_CR419","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.neucom.2017.01.023","volume":"237","author":"X Sui","year":"2017","unstructured":"Sui X, Zheng Y, Wei B, Bi H, Wu J, Pan X, Yin Y, Zhang S (2017) Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks. Neurocomputing 237:332\u2013341. https:\/\/doi.org\/10.1016\/j.neucom.2017.01.023","journal-title":"Neurocomputing"},{"key":"10736_CR420","doi-asserted-by":"crossref","unstructured":"Sun X, Xu Y, Zhao W, You T, Liu J (2018) Optic disc segmentation from retinal fundus images via deep object detection networks. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5954\u20135957","DOI":"10.1109\/EMBC.2018.8513592"},{"issue":"9","key":"10736_CR421","doi-asserted-by":"publisher","first-page":"096004","DOI":"10.1117\/1.JBO.25.9.096004","volume":"25","author":"Y Sun","year":"2020","unstructured":"Sun Y, Zhang H, Yao X (2020) Automatic diagnosis of macular diseases from OCT volume based on its two-dimensional feature map and convolutional neural network with attention mechanism. J Biomed Opt 25(9):096004","journal-title":"J Biomed Opt"},{"key":"10736_CR422","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1109\/LSP.2022.3151549","volume":"29","author":"JD Sun","year":"2022","unstructured":"Sun JD, Yao C, Liu J, Liu W, Yu ZK (2022) GNAS-U$$^2$$ Net: a new optic cup and optic disc segmentation architecture with genetic neural architecture search. IEEE Signal Process Lett 29:697\u2013701","journal-title":"IEEE Signal Process Lett"},{"key":"10736_CR423","doi-asserted-by":"crossref","unstructured":"Surendiran J, Theetchenya S, Benson Mansingh P, Sekar G, Dhipa M, Yuvaraj N, Arulkarthick V, Suresh C, Sriram A, Srihari K et al (2022) Segmentation of optic disc and cup using modified recurrent neural network. BioMed Res Int 2022","DOI":"10.1155\/2022\/6799184"},{"key":"10736_CR425","doi-asserted-by":"crossref","unstructured":"Swarnalatha K, Nayak UA, Benny NA, Bharath H, Shetty D, Kumar SD (2023) Detection of diabetic retinopathy using convolution neural network. In: Emerging research in computing. information, communication and applications. Springer, pp 427\u2013439","DOI":"10.1007\/978-981-19-5482-5_37"},{"key":"10736_CR426","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.jocs.2017.02.006","volume":"20","author":"JH Tan","year":"2017","unstructured":"Tan JH, Acharya UR, Bhandary SV, Chua KC, Sivaprasad S (2017) Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J Comput Sci 20:70\u201379","journal-title":"J Comput Sci"},{"issue":"9","key":"10736_CR427","doi-asserted-by":"publisher","first-page":"2238","DOI":"10.1109\/TMI.2022.3161681","volume":"41","author":"Y Tan","year":"2022","unstructured":"Tan Y, Yang KF, Zhao SX, Li YJ (2022) Retinal vessel segmentation with skeletal prior and contrastive loss. IEEE Trans Med Imaging 41(9):2238\u20132251","journal-title":"IEEE Trans Med Imaging"},{"issue":"9","key":"10736_CR428","doi-asserted-by":"publisher","first-page":"e0162001","DOI":"10.1371\/journal.pone.0162001","volume":"11","author":"L Terry","year":"2016","unstructured":"Terry L, Cassels N, Lu K, Acton JH, Margrain TH, North RV, Fergusson J, White N, Wood A (2016) Automated retinal layer segmentation using spectral domain optical coherence tomography: evaluation of inter-session repeatability and agreement between devices. PLoS One 11(9):e0162001. https:\/\/doi.org\/10.1371\/journal.pone.0162001","journal-title":"PLoS One"},{"issue":"11","key":"10736_CR429","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","volume":"121","author":"YC Tham","year":"2014","unstructured":"Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY (2014) Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121(11):2081\u20132090","journal-title":"Ophthalmology"},{"key":"10736_CR431","unstructured":"Tian Y, Krishnan D, Isola P (2019) Contrastive representation distillation. Preprint arXiv:1910.10699"},{"issue":"6","key":"10736_CR432","doi-asserted-by":"publisher","first-page":"3043","DOI":"10.1364\/BOE.390056","volume":"11","author":"Z Tian","year":"2020","unstructured":"Tian Z, Zheng Y, Li X, Du S, Xu X (2020) Graph convolutional network based optic disc and cup segmentation on fundus images. Biomed Opt Express 11(6):3043\u20133057","journal-title":"Biomed Opt Express"},{"issue":"22","key":"10736_CR433","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","volume":"318","author":"DSW Ting","year":"2017","unstructured":"Ting DSW et al (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22):2211\u20132223","journal-title":"JAMA"},{"issue":"103","key":"10736_CR434","first-page":"053","volume":"70","author":"B Topta\u015f","year":"2021","unstructured":"Topta\u015f B, Hanbay D (2021) Retinal blood vessel segmentation using pixel-based feature vector. Biomed Signal Process Control 70(103):053","journal-title":"Biomed Signal Process Control"},{"issue":"9","key":"10736_CR435","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1111\/jdi.13860","volume":"13","author":"M Tosur","year":"2022","unstructured":"Tosur M, Philipson LH (2022) Precision diabetes: lessons learned from maturity-onset diabetes of the young (MODY). J Diabetes Investig 13(9):1465\u20131471","journal-title":"J Diabetes Investig"},{"issue":"1","key":"10736_CR436","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJACI.313965","volume":"13","author":"R Touahri","year":"2022","unstructured":"Touahri R, Azizi N, Hammami NE, Benaida F, Zemmal N, Gasmi I (2022) An improved disc segmentation based on U-Net architecture for glaucoma diagnosis. Int J Ambient Comput Intell 13(1):1\u201318","journal-title":"Int J Ambient Comput Intell"},{"issue":"12","key":"10736_CR437","doi-asserted-by":"publisher","first-page":"7600","DOI":"10.1167\/iovs.12-10449","volume":"53","author":"K Tran","year":"2012","unstructured":"Tran K, Mendel TA, Holbrook KL, Yates PA (2012) Construction of an inexpensive, hand-held fundus camera through modification of a consumer point-and-shoot camera. Investig Ophthalmol Vis Sci 53(12):7600\u20137607","journal-title":"Investig Ophthalmol Vis Sci"},{"issue":"5","key":"10736_CR438","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1167\/tvst.7.5.19","volume":"7","author":"AD Treister","year":"2018","unstructured":"Treister AD, Nesper PL, Fayed AE, Gill MK, Mirza RG, Fawzi AA (2018) Prevalence of subclinical CNV and choriocapillaris nonperfusion in fellow eyes of unilateral exudative AMD on OCT angiography. Transl Vis Sci Technol 7(5):19","journal-title":"Transl Vis Sci Technol"},{"issue":"2","key":"10736_CR439","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1016\/j.bbe.2021.05.011","volume":"41","author":"A Tulsani","year":"2021","unstructured":"Tulsani A, Kumar P, Pathan S (2021) Automated segmentation of optic disc and optic cup for glaucoma assessment using improved unet++ architecture. Biocybern Biomed Eng 41(2):819\u2013832","journal-title":"Biocybern Biomed Eng"},{"issue":"100","key":"10736_CR440","first-page":"954","volume":"84","author":"A Uemura","year":"2021","unstructured":"Uemura A, Fruttiger M, D\u2019Amore PA, De Falco S, Joussen AM, Sennlaub F, Brunck LR, Johnson KT, Lambrou GN, Rittenhouse KD et al (2021) VEGFR1 signaling in retinal angiogenesis and microinflammation. Prog Retin Eye Res 84(100):954","journal-title":"Prog Retin Eye Res"},{"key":"10736_CR441","doi-asserted-by":"crossref","unstructured":"Usman A, Khitran SA, Usman Akram M, Nadeem Y (2014) A robust algorithm for optic disc segmentation from colored fundus images. In: International conference image analysis and recognition. Springer, pp 303\u2013310","DOI":"10.1007\/978-3-319-11755-3_34"},{"issue":"3","key":"10736_CR442","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s11831-016-9174-3","volume":"24","author":"M Usman","year":"2017","unstructured":"Usman M, Fraz MM, Barman SA (2017) Computer vision techniques applied for diagnostic analysis of retinal OCT Images: a review. Arch Comput Methods Eng 24(3):449\u2013465","journal-title":"Arch Comput Methods Eng"},{"issue":"100","key":"10736_CR443","first-page":"770","volume":"73","author":"TJ Van Rijssen","year":"2019","unstructured":"Van Rijssen TJ, Van Dijk EH, Yzer S, Ohno-Matsui K, Keunen JE, Schlingemann RO, Sivaprasad S, Querques G, Downes SM, Fauser S et al (2019) Central serous chorioretinopathy: towards an evidence-based treatment guideline. Prog Retin Eye Res 73(100):770","journal-title":"Prog Retin Eye Res"},{"issue":"2","key":"10736_CR444","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1097\/ICU.0000000000000733","volume":"32","author":"LE Vazquez","year":"2021","unstructured":"Vazquez LE, Bye A, Aref AA (2021) Recent developments in the use of optical coherence tomography for glaucoma. Curr Opin Ophthalmol 32(2):98\u2013104","journal-title":"Curr Opin Ophthalmol"},{"key":"10736_CR445","doi-asserted-by":"crossref","unstructured":"Vergroesen JE, Thee EF, Ahmadizar F, van Duijn CM, Stricker BH, Kavousi M, Klaver CC, Ramdas WD (2022) Association of diabetes medication with open-angle glaucoma, age-related macular degeneration, and cataract in the rotterdam study. JAMA Ophthalmol","DOI":"10.1001\/jamaophthalmol.2022.1435"},{"issue":"5","key":"10736_CR446","doi-asserted-by":"publisher","first-page":"2016","DOI":"10.3390\/s22052016","volume":"22","author":"IA Viedma","year":"2022","unstructured":"Viedma IA, Alonso-Caneiro D, Read SA, Collins MJ (2022) OCT retinal and choroidal layer instance segmentation using mask R-CNN. Sensors 22(5):2016","journal-title":"Sensors"},{"issue":"2","key":"10736_CR447","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41433-022-02056-9","volume":"37","author":"S Vujosevic","year":"2023","unstructured":"Vujosevic S, Parra MM, Hartnett ME, O\u2019Toole L, Nuzzi A, Limoli C, Villani E, Nucci P (2023) Optical coherence tomography as retinal imaging biomarker of neuroinflammation\/neurodegeneration in systemic disorders in adults and children. Eye 37(2):203\u2013219","journal-title":"Eye"},{"key":"10736_CR448","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.compeleceng.2018.07.042","volume":"72","author":"S Wan","year":"2018","unstructured":"Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput Electr Eng 72:274\u2013282","journal-title":"Comput Electr Eng"},{"issue":"5","key":"10736_CR449","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1007\/s10792-021-02137-w","volume":"42","author":"P W\u00e4ndell","year":"2022","unstructured":"W\u00e4ndell P, Carlsson AC, Ljunggren G (2022) Systemic diseases and their association with open-angle glaucoma in the population of stockholm. Int Ophthalmol 42(5):1481\u20131489","journal-title":"Int Ophthalmol"},{"key":"10736_CR450","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang L (2022) Optic disc segmentation in retinal fundus images using improved ce-net. In: Fourteenth international conference on digital image processing (ICDIP 2022). SPIE, vol 12342, pp 417\u2013426","DOI":"10.1117\/12.2643259"},{"key":"10736_CR451","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.neucom.2014.07.059","volume":"149","author":"S Wang","year":"2015","unstructured":"Wang S, Yin Y, Cao G, Wei B, Zheng Y, Yang G (2015) Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149:708\u2013717","journal-title":"Neurocomputing"},{"issue":"12","key":"10736_CR452","doi-asserted-by":"publisher","first-page":"4928","DOI":"10.1364\/BOE.7.004928","volume":"7","author":"Y Wang","year":"2016","unstructured":"Wang Y, Zhang Y, Yao Z, Zhao R, Zhou F (2016) Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images. Biomed Opt Express 7(12):4928\u20134940","journal-title":"Biomed Opt Express"},{"issue":"2","key":"10736_CR453","doi-asserted-by":"publisher","first-page":"168","DOI":"10.3390\/e21020168","volume":"21","author":"C Wang","year":"2019","unstructured":"Wang C, Zhao Z, Ren Q, Xu Y, Yu Y (2019) Dense U-Net based on patch-based learning for retinal vessel segmentation. Entropy 21(2):168","journal-title":"Entropy"},{"key":"10736_CR454","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.bspc.2019.01.022","volume":"51","author":"L Wang","year":"2019","unstructured":"Wang L, Liu H, Lu Y, Chen H, Zhang J, Pu J (2019) A coarse-to-fine deep learning framework for optic disc segmentation in fundus images. Biomed Signal Process Control 51:82\u201389","journal-title":"Biomed Signal Process Control"},{"issue":"7","key":"10736_CR455","doi-asserted-by":"publisher","first-page":"5519","DOI":"10.1007\/s00500-020-05552-w","volume":"25","author":"C Wang","year":"2021","unstructured":"Wang C, Zhao Z, Yu Y (2021) Fine retinal vessel segmentation by combining nest U-Net and patch-learning. Soft Comput 25(7):5519\u20135532","journal-title":"Soft Comput"},{"issue":"21","key":"10736_CR456","doi-asserted-by":"publisher","first-page":"215006","DOI":"10.1088\/1361-6560\/ac2dd1","volume":"66","author":"J Wang","year":"2021","unstructured":"Wang J, He Y, Fang W, Chen Y, Li W, Shi G (2021) Unsupervised domain adaptation model for lesion detection in retinal OCT images. Phys Med Biol 66(21):215006","journal-title":"Phys Med Biol"},{"issue":"8","key":"10736_CR457","doi-asserted-by":"publisher","first-page":"4713","DOI":"10.1364\/BOE.426803","volume":"12","author":"J Wang","year":"2021","unstructured":"Wang J, Li W, Chen Y, Fang W, Kong W, He Y, Shi G (2021) Weakly supervised anomaly segmentation in retinal oct images using an adversarial learning approach. Biomed Opt Express 12(8):4713\u20134729","journal-title":"Biomed Opt Express"},{"issue":"104","key":"10736_CR458","first-page":"116","volume":"128","author":"J Wang","year":"2021","unstructured":"Wang J, Li YJ, Yang KF (2021) Retinal fundus image enhancement with image decomposition and visual adaptation. Comput Biol Med 128(104):116","journal-title":"Comput Biol Med"},{"issue":"03","key":"10736_CR459","doi-asserted-by":"publisher","first-page":"2250019","DOI":"10.1142\/S1793545822500195","volume":"15","author":"L Wang","year":"2022","unstructured":"Wang L, Li X, Chen Y, Han D, Wang M, Zeng Y, Zhong J, Wang X, Ji Y, Xiong H et al (2022) Automated retinal layer segmentation in optical coherence tomography images with intraretinal fluid. J Innov Opt Health Sci 15(03):2250019","journal-title":"J Innov Opt Health Sci"},{"key":"10736_CR461","doi-asserted-by":"publisher","first-page":"101066","DOI":"10.1016\/j.preteyeres.2022.101066","volume":"90","author":"Z Wang","year":"2022","unstructured":"Wang Z, Wiggs JL, Aung T, Khawaja AP, Khor CC (2022) The genetic basis for adult onset glaucoma: recent advances and future directions.\nProg Retin Eye Res 90:101066","journal-title":"Prog Retin Eye Res"},{"issue":"5","key":"10736_CR462","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1109\/TMI.2021.3130987","volume":"41","author":"Q Wang","year":"2022","unstructured":"Wang Q et al (2022) Deep fusion of OCT and fundus images for improved early detection of retinal diseases. IEEE Trans Med Imaging 41(5):1320\u20131331","journal-title":"IEEE Trans Med Imaging"},{"key":"10736_CR464","doi-asserted-by":"publisher","first-page":"60929","DOI":"10.1109\/ACCESS.2020.2983818","volume":"8","author":"H Wei","year":"2020","unstructured":"Wei H, Peng P (2020) The segmentation of retinal layer and fluid in SD-OCT images using mutex dice loss based fully convolutional networks. IEEE Access 8:60929\u201360939","journal-title":"IEEE Access"},{"issue":"1","key":"10736_CR465","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0014-4835(95)80053-0","volume":"61","author":"RS Weinhaus","year":"1995","unstructured":"Weinhaus RS, Burke JM, Delori FC, Snodderly DM (1995) Comparison of fluorescein angiography with microvascular anatomy of macaque retinas. Exp Eye Res 61(1):1\u201316","journal-title":"Exp Eye Res"},{"key":"10736_CR466","doi-asserted-by":"crossref","unstructured":"Wei Z, Yuhan P, Jianhang J, Jikun Y, Weiqi B, Yugen Y, Wenle W (2022) RMSDSC-Net: a robust multiscale feature extraction with depthwise separable convolution network for optic disc and cup segmentation. Int J Intell Syst","DOI":"10.1002\/int.23051"},{"issue":"4","key":"10736_CR467","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1109\/TBME.2012.2184759","volume":"59","author":"GR Wilkins","year":"2012","unstructured":"Wilkins GR, Houghton OM, Oldenburg AL (2012) Automated segmentation of intraretinal cystoid fluid in optical coherence tomography. IEEE Trans Biomed Eng 59(4):1109\u20131114","journal-title":"IEEE Trans Biomed Eng"},{"key":"10736_CR468","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1111\/j.1442-9071.2010.02363.x","volume":"38","author":"CE Willoughby","year":"2010","unstructured":"Willoughby CE, Ponzin D, Lobo SFA, Landau K, Omidi Y (2010) Anatomy and physiology of the human eye: effects of mucopolysaccharidoses disease on structure and function\u2013a review. Clin Exp Ophthalmol 38:2\u201311","journal-title":"Clin Exp Ophthalmol"},{"key":"10736_CR469","unstructured":"Wong RV. Macular edema: so many types. https:\/\/retinaeyedoctor.com\/2010\/02\/what-is-macular-edema\/. Accessed 5 April 2022"},{"key":"10736_CR470","unstructured":"World Health Organization Blindness and visual impairment. https:\/\/www.who.int\/westernpacific\/health-topics\/blindness-and-vision-loss. Accessed 24 Dec 2020"},{"key":"10736_CR471","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.neunet.2020.02.018","volume":"126","author":"Y Wu","year":"2020","unstructured":"Wu Y, Xia Y, Song Y, Zhang Y, Cai W (2020) NFN+: a novel network followed network for retinal vessel segmentation. Neural Netw 126:153\u2013162","journal-title":"Neural Netw"},{"issue":"113","key":"10736_CR472","first-page":"700","volume":"196","author":"Y Wu","year":"2022","unstructured":"Wu Y, Szymanska M, Hu Y, Fazal MI, Jiang N, Yetisen AK, Cordeiro MF (2022) Measures of disease activity in glaucoma. Biosens Bioelectron 196(113):700","journal-title":"Biosens Bioelectron"},{"issue":"12","key":"10736_CR473","doi-asserted-by":"publisher","first-page":"5880","DOI":"10.1109\/TIP.2018.2860255","volume":"27","author":"D Xiang","year":"2018","unstructured":"Xiang D, Tian H, Yang X, Shi F, Zhu W, Chen H, Chen X (2018) Automatic segmentation of retinal layer in oct images with choroidal neovascularization. IEEE Trans Image Process 27(12):5880\u20135891","journal-title":"IEEE Trans Image Process"},{"key":"10736_CR474","doi-asserted-by":"crossref","unstructured":"Xie S, Tu Z (2015) Holistically-nested edge detection. In: Proceedings of the IEEE international conference on computer vision, pp 1395\u20131403","DOI":"10.1109\/ICCV.2015.164"},{"key":"10736_CR475","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.compmedimag.2015.10.003","volume":"47","author":"L Xiong","year":"2016","unstructured":"Xiong L, Li H (2016) An approach to locate optic disc in retinal images with pathological changes. Comput Med Imaging Graph 47:40\u201350","journal-title":"Comput Med Imaging Graph"},{"issue":"102","key":"10736_CR476","first-page":"261","volume":"126","author":"H Xiong","year":"2022","unstructured":"Xiong H, Liu S, Sharan RV, Coiera E, Berkovsky S (2022) Weak label based Bayesian u-net for optic disc segmentation in fundus images. Artif Intell Med 126(102):261","journal-title":"Artif Intell Med"},{"key":"10736_CR477","doi-asserted-by":"crossref","unstructured":"Xiong K, Wang L, Li W, Wang W, Meng J, Gong X, Lu P, Liang X, Huang J, Huang W (2022b) Risk of acute angle-closure and changes in intraocular pressure after pupillary dilation in patients with diabetes. Eye 1\u20136","DOI":"10.1038\/s41433-022-02215-y"},{"issue":"2","key":"10736_CR478","first-page":"210","volume":"126","author":"L Xu","year":"2019","unstructured":"Xu L et al (2019) High myopia and glaucoma susceptibility: the Beijing eye study. Ophthalmology 126(2):210\u2013217","journal-title":"Ophthalmology"},{"key":"10736_CR479","doi-asserted-by":"publisher","first-page":"786425","DOI":"10.3389\/fbioe.2021.786425","volume":"9","author":"S Xu","year":"2021","unstructured":"Xu S, Chen Z, Cao W, Zhang F, Tao B (2021) Retinal vessel segmentation algorithm based on residual convolution neural network. Front Bioeng Biotechnol 9:786425","journal-title":"Front Bioeng Biotechnol"},{"key":"10736_CR480","doi-asserted-by":"crossref","unstructured":"Xu R, Zhao J, Ye X, Wu P, Wang Z, Li H, Chen YW (2022) Local-region and cross-dataset contrastive learning for retinal vessel segmentation. In: Medical image computing and computer assisted intervention\u2013MICCAI 2022: 25th international conference, Singapore, Proceedings, Part II. Springer, pp 571\u2013581","DOI":"10.1007\/978-3-031-16434-7_55"},{"issue":"3","key":"10736_CR481","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.cmpb.2014.05.002","volume":"116","author":"SF Yang","year":"2014","unstructured":"Yang SF, Cheng CH (2014) Fast computation of hessian-based enhancement filters for medical images. Comput Methods Programs Biomed 116(3):215\u2013225","journal-title":"Comput Methods Programs Biomed"},{"issue":"20","key":"10736_CR482","doi-asserted-by":"publisher","first-page":"21293","DOI":"10.1364\/oe.18.021293","volume":"18","author":"Q Yang","year":"2010","unstructured":"Yang Q, Reisman CA, Wang Z, Fukuma Y, Hangai M, Yoshimura N, Tomidokoro A, Araie M, Raza AS, Hood DC, Chan K (2010) Automated layer segmentation of macular OCT images using dual-scale gradient information. Optics Express 18(20):21293. https:\/\/doi.org\/10.1364\/oe.18.021293","journal-title":"Optics Express"},{"key":"10736_CR483","doi-asserted-by":"crossref","unstructured":"Yang S, Zhou X, Wang J, Xie G, Lv C, Gao P, Lv B (2020) Unsupervised domain adaptation for cross-device oct lesion detection via learning adaptive features. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). IEEE, pp 1570\u20131573","DOI":"10.1109\/ISBI45749.2020.9098380"},{"issue":"154","key":"10736_CR484","first-page":"712","volume":"117","author":"G Yang","year":"2021","unstructured":"Yang G, Wei J, Liu P, Zhang Q, Tian Y, Hou G, Meng L, Xin Y, Jiang X (2021) Role of the gut microbiota in type 2 diabetes and related diseases. Metabolism 117(154):712","journal-title":"Metabolism"},{"key":"10736_CR486","doi-asserted-by":"crossref","unstructured":"Ye EZ, Ye J, Ye EH (2023) Applications of vision transformers in retinal imaging: a systematic review. Authorea","DOI":"10.22541\/au.167528318.80645903\/v1"},{"issue":"1","key":"10736_CR487","doi-asserted-by":"publisher","first-page":"e12361","DOI":"10.1016\/j.heliyon.2022.e12361","volume":"9","author":"S Yi","year":"2023","unstructured":"Yi S, Wei Y, Zhang G, Wang T, She F, Yang X (2023) Segmentation of retinal vessels based on MRANet. Heliyon 9(1):e12361","journal-title":"Heliyon"},{"key":"10736_CR488","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.inffus.2021.09.010","volume":"78","author":"P Yin","year":"2022","unstructured":"Yin P, Cai H, Wu Q (2022) DF-Net: deep fusion network for multi-source vessel segmentation. Inform Fusion 78:199\u2013208","journal-title":"Inform Fusion"},{"key":"10736_CR489","doi-asserted-by":"publisher","first-page":"922289","DOI":"10.3389\/fpubh.2022.922289","volume":"10","author":"Z Yongpeng","year":"2022","unstructured":"Yongpeng Z, Yaxing W, Jinqiong Z, Qian W, Yanni Y, Xuan Y, Jingyan Y, Wenjia Z, Ping W, Chang S et al (2022) The association between diabetic retinopathy and the prevalence of age-related macular degeneration\u2013the Kailuan eye study. Front Public Health 10:922289","journal-title":"Front Public Health"},{"key":"10736_CR490","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s11517-021-02321-1","volume":"59","author":"TK Yoo","year":"2021","unstructured":"Yoo TK, Choi JY, Kim HK (2021) Feasibility study to improve deep learning in oct diagnosis of rare retinal diseases with few-shot classification. Med Biol Eng Comput 59:401\u2013415","journal-title":"Med Biol Eng Comput"},{"issue":"6","key":"10736_CR492","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1016\/j.ophtha.2016.02.017","volume":"123","author":"M Yu","year":"2016","unstructured":"Yu M, Lin C, Weinreb RN, Lai G, Chiu V, Leung CKS (2016) Risk of visual field progression in glaucoma patients with progressive retinal nerve fiber layer thinning: a 5-year prospective study. Ophthalmology 123(6):1201\u20131210","journal-title":"Ophthalmology"},{"issue":"2","key":"10736_CR493","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1111\/ceo.14044","volume":"50","author":"C Yuksel Elgin","year":"2022","unstructured":"Yuksel Elgin C, Chen D, Al-Aswad LA (2022) Ophthalmic imaging for the diagnosis and monitoring of glaucoma: a review. Clin Exp Ophthalmol 50(2):183\u2013197","journal-title":"Clin Exp Ophthalmol"},{"issue":"106","key":"10736_CR494","first-page":"067","volume":"150","author":"N Zaaboub","year":"2022","unstructured":"Zaaboub N, Sandid F, Douik A, Solaiman B (2022) Optic disc detection and segmentation using saliency mask in retinal fundus images. Comput Biol Med 150(106):067","journal-title":"Comput Biol Med"},{"issue":"103","key":"10736_CR495","first-page":"537","volume":"116","author":"GT Zago","year":"2020","unstructured":"Zago GT, Andre\u00e3o RV, Dorizzi B, Salles EOT (2020) Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Comput Biol Med 116(103):537","journal-title":"Comput Biol Med"},{"issue":"4","key":"10736_CR496","doi-asserted-by":"publisher","first-page":"514","DOI":"10.3390\/jpm12040514","volume":"12","author":"AC Zaharia","year":"2022","unstructured":"Zaharia AC, Dumitrescu OM, Radu M, Rogoz RE (2022) Adherence to therapy in glaucoma treatment\u2013a review. J Pers Med 12(4):514","journal-title":"J Pers Med"},{"issue":"8","key":"10736_CR497","doi-asserted-by":"publisher","first-page":"4340","DOI":"10.1364\/BOE.10.004340","volume":"10","author":"P Zang","year":"2019","unstructured":"Zang P, Wang J, Hormel TT, Liu L, Huang D, Jia Y (2019) Automated segmentation of peripapillary retinal boundaries in oct combining a convolutional neural network and a multi-weights graph search. Biomed Opt Express 10(8):4340\u20134352","journal-title":"Biomed Opt Express"},{"issue":"2","key":"10736_CR498","first-page":"201","volume":"45","author":"Y Zhang","year":"2023","unstructured":"Zhang Y et al (2023) Multi-modal AI system for retinal image analysis: improving segmentation and disease detection. J Ophthalmic Res 45(2):201\u2013215","journal-title":"J Ophthalmic Res"},{"issue":"108","key":"10736_CR499","first-page":"400","volume":"192","author":"S Zhang","year":"2022","unstructured":"Zhang S, Webers CA, Berendschot TT (2022) A double-pass fundus reflection model for efficient single retinal image enhancement. Signal Process 192(108):400","journal-title":"Signal Process"},{"issue":"3","key":"10736_CR500","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1007\/s00417-021-05402-x","volume":"260","author":"X Zhang","year":"2022","unstructured":"Zhang X, Li D, Wei Q, Han X, Zhang B, Chen H, Zhang Y, Mo B, Hu B, Ding D et al (2022) Automated detection of severe diabetic retinopathy using deep learning method. Graefes Arch Clin Exp Ophthalmol 260(3):849\u2013856","journal-title":"Graefes Arch Clin Exp Ophthalmol"},{"key":"10736_CR501","doi-asserted-by":"crossref","unstructured":"Zhang X, Song QJ, Wang RC, Zhou Z (2022c) Convolutional autoencoder joint boundary and mask adversarial learning for fundus image segmentation. Front Hum Neurosci 834","DOI":"10.3389\/fnhum.2022.1043569"},{"issue":"103","key":"10736_CR502","first-page":"472","volume":"73","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Fang J, Chen Y, Jia L (2022) Edge-aware u-net with gated convolution for retinal vessel segmentation. Biomed Signal Process Control 73(103):472","journal-title":"Biomed Signal Process Control"},{"issue":"116","key":"10736_CR503","first-page":"526","volume":"195","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, He M, Chen Z, Hu K, Li X, Gao X (2022) Bridge-net: Context-involved u-net with patch-based loss weight mapping for retinal blood vessel segmentation. Expert Syst Appl 195(116):526","journal-title":"Expert Syst Appl"},{"issue":"7","key":"10736_CR504","first-page":"613","volume":"35","author":"YP Zhang","year":"2022","unstructured":"Zhang YP, Wang YX, Zhou JQ, Qian W, Yan YN, Xuan Y, Yang JY, Zhou WJ, Ping W, Chang S et al (2022) The influence of diabetes, hypertension, and hyperlipidemia on the onset of age-related macular degeneration in north china: the Kailuan eye study. Biomed Environ Sci 35(7):613\u2013621","journal-title":"Biomed Environ Sci"},{"key":"10736_CR505","doi-asserted-by":"crossref","unstructured":"Zhang Z, Yin FS, Liu J, Wong WK, Tan NM, Lee BH, Cheng J, Wong TY (2010) Origa-light: an online retinal fundus image database for glaucoma analysis and research. In: 2010 annual international conference of the IEEE engineering in medicine and biology. IEEE, pp 3065\u20133068","DOI":"10.1109\/IEMBS.2010.5626137"},{"key":"10736_CR506","doi-asserted-by":"crossref","unstructured":"Zhang L, Zhu W, Shi F, Chen H, Chen X (2015) Automated segmentation of intraretinal cystoid macular edema for retinal 3d OCT images with macular hole. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI). IEEE, pp 1494\u20131497","DOI":"10.1109\/ISBI.2015.7164160"},{"issue":"4","key":"10736_CR507","doi-asserted-by":"publisher","first-page":"e0127486","DOI":"10.1371\/journal.pone.0127486","volume":"10","author":"Y Zhao","year":"2015","unstructured":"Zhao Y, Liu Y, Wu X, Harding SP, Zheng Y (2015) Correction: retinal vessel segmentation: an efficient graph cut approach with retinex and local phase. PLoS One 10(4):e0127486","journal-title":"PLoS One"},{"issue":"4","key":"10736_CR508","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1109\/JBHI.2019.2934477","volume":"24","author":"R Zhao","year":"2019","unstructured":"Zhao R, Chen X, Liu X, Chen Z, Guo F, Li S (2019) Direct cup-to-disc ratio estimation for glaucoma screening via semi-supervised learning. IEEE J Biomed Health Inform 24(4):1104\u20131113","journal-title":"IEEE J Biomed Health Inform"},{"key":"10736_CR509","doi-asserted-by":"crossref","unstructured":"Zhuang J, Chen Z, Zhang J, Zhang D, Cai Z (2019) Domain adaptation for retinal vessel segmentation using asymmetrical maximum classifier discrepancy. In: Proceedings of the ACM turing celebration conference-China, pp 1\u20136","DOI":"10.1145\/3321408.3322627"},{"key":"10736_CR510","doi-asserted-by":"crossref","unstructured":"Zhu H, Zhu X, Liu Y, Jiang F, Chen M, Cheng L, Cheng X (2020) Gene expression profiling of type 2 diabetes mellitus by bioinformatics analysis. Comput Math Methods Med 2020","DOI":"10.1155\/2020\/9602016"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10736-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-10736-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10736-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T06:08:27Z","timestamp":1715926107000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-10736-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,5]]},"references-count":480,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["10736"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-10736-z","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,5]]},"assertion":[{"value":"5 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declare that there are no competing interests related to this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"111"}}