{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T22:13:21Z","timestamp":1784326401450,"version":"3.55.0"},"reference-count":108,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100006203","name":"Johnson Space Center","doi-asserted-by":"publisher","award":["80NSSC20K183"],"award-info":[{"award-number":["80NSSC20K183"]}],"id":[{"id":"10.13039\/100006203","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Graefes Arch Clin Exp Ophthalmol"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>Generative adversarial networks (GANs) are key components of many artificial intelligence (AI) systems that are applied to image-informed bioengineering and medicine. GANs combat key limitations facing deep learning models: small, unbalanced datasets containing few images of severe disease. The predictive capacity of conditional GANs may also be extremely useful in managing disease on an individual basis. This narrative review focusses on the application of GANs in ophthalmology, in order to provide a critical account of the current state and ongoing challenges for healthcare professionals and allied scientists who are interested in this rapidly evolving field.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We performed a search of studies that apply generative adversarial networks (GANs) in\u00a0diagnosis, therapy and prognosis of eight eye diseases. These disparate tasks were selected to highlight developments in GAN techniques, differences and common features to aid practitioners and future adopters in the field of ophthalmology.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The studies we identified show that GANs have demonstrated capacity to: generate realistic and useful synthetic images, convert image modality, improve image quality, enhance extraction of relevant features, and provide prognostic predictions based on input images and other relevant data.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The broad range of architectures considered describe how GAN technology is evolving to meet different challenges (including segmentation and multi-modal imaging) that are of particular relevance to ophthalmology. The wide availability of datasets now facilitates the entry of new researchers to the field. However mainstream adoption of GAN technology for clinical use remains contingent on larger public datasets for widespread validation and necessary regulatory oversight.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s00417-025-06830-9","type":"journal-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T16:26:06Z","timestamp":1745339166000},"page":"2117-2134","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Applications of generative adversarial networks in the diagnosis, prognosis, and treatment of ophthalmic diseases"],"prefix":"10.1007","volume":"263","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1403-0664","authenticated-orcid":false,"given":"Robert","family":"Doorly","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Ong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ethan","family":"Waisberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prithul","family":"Sarker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasif","family":"Zaman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Tavakkoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew G.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"issue":"7","key":"6830_CR1","doi-asserted-by":"publisher","first-page":"101095","DOI":"10.1016\/j.xcrm.2023.101095","volume":"4","author":"Z Li","year":"2023","unstructured":"Li Z, Wang L, Wu X, Jiang J, Qiang W, Xie H et al (2023) Artificial intelligence in ophthalmology: The path to the real-world clinic. Cell Rep Med 4(7):101095","journal-title":"Cell Rep Med"},{"key":"6830_CR2","doi-asserted-by":"crossref","unstructured":"Waisberg E, Ong J, Paladugu P, Kamran SA, Zaman N, Lee AG et al (2022) Challenges of artificial intelligence in space medicine. Space: Sci Technol 2022(2):1\u20137","DOI":"10.34133\/2022\/9852872"},{"issue":"4","key":"6830_CR3","doi-asserted-by":"publisher","first-page":"100888","DOI":"10.1016\/j.imr.2022.100888","volume":"11","author":"AS Ahuja","year":"2022","unstructured":"Ahuja AS, Wagner IV, Dorairaj S, Checo L, Ten Hulzen R (2022) Artificial intelligence in ophthalmology: A multidisciplinary approach. Integ Med Res 11(4):100888","journal-title":"Integ Med Res"},{"issue":"6","key":"6830_CR4","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker IH (2021) Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci 2(6):420","journal-title":"SN Comput Sci"},{"issue":"22","key":"6830_CR5","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 et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402","journal-title":"JAMA"},{"issue":"6","key":"6830_CR6","doi-asserted-by":"publisher","first-page":"e0233514","DOI":"10.1371\/journal.pone.0233514","volume":"15","author":"M Shaban","year":"2020","unstructured":"Shaban M, Ogur Z, Mahmoud A, Switala A, Shalaby A, Abu Khalifeh H et al (2020) A convolutional neural network for the screening and staging of diabetic retinopathy. PLoS ONE 15(6):e0233514","journal-title":"PLoS ONE"},{"key":"6830_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04001-1","volume":"22","author":"Y-M Chen","year":"2021","unstructured":"Chen Y-M, Huang W-T, Ho W-H, Tsai J-T (2021) Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning. BMC Bioinform 22:1\u201316","journal-title":"BMC Bioinform"},{"issue":"5","key":"6830_CR8","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 et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 172(5):1122\u2013319","journal-title":"Cell."},{"key":"6830_CR9","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611\u2013629","journal-title":"Insights Imaging"},{"issue":"11","key":"6830_CR10","doi-asserted-by":"publisher","first-page":"2345","DOI":"10.1016\/j.ophtha.2016.07.020","volume":"123","author":"J Kalpathy-Cramer","year":"2016","unstructured":"Kalpathy-Cramer J, Campbell JP, Erdogmus D, Tian P, Kedarisetti D, Moleta C et al (2016) Plus disease in retinopathy of prematurity: improving diagnosis by ranking disease severity and using quantitative image analysis. Ophthalmology 123(11):2345\u20132351","journal-title":"Ophthalmology"},{"issue":"5","key":"6830_CR11","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1097\/ICU.0000000000000680","volume":"31","author":"RH Gensure","year":"2020","unstructured":"Gensure RH, Chiang MF, Campbell JP (2020) Artificial intelligence for retinopathy of prematurity. Curr Opin Ophthalmol 31(5):312\u2013317","journal-title":"Curr Opin Ophthalmol"},{"issue":"13","key":"6830_CR12","doi-asserted-by":"publisher","first-page":"2834","DOI":"10.1038\/s41433-023-02400-7","volume":"37","author":"J Ong","year":"2023","unstructured":"Ong J, Waisberg E, Kamran SA, Paladugu P, Zaman N, Sarker P et al (2023) Deep learning synthetic angiograms for individuals unable to undergo contrast-guided laser treatment in aggressive retinopathy of prematurity. Eye 37(13):2834\u20132835","journal-title":"Eye"},{"issue":"4","key":"6830_CR13","doi-asserted-by":"publisher","first-page":"100493","DOI":"10.1016\/j.xops.2024.100493","volume":"4","author":"SA Kamran","year":"2024","unstructured":"Kamran SA, Hossain KF, Ong J, Waisberg E, Zaman N, Baker SA et al (2024) FA4SANS-GAN: A novel machine learning generative adversarial network to further understand ophthalmic changes in Spaceflight Associated Neuro-Ocular Syndrome (SANS). Ophthalmol Sci 4(4):100493","journal-title":"Ophthalmol Sci"},{"key":"6830_CR14","doi-asserted-by":"publisher","first-page":"1184892","DOI":"10.3389\/fmed.2023.1184892","volume":"10","author":"Z Wang","year":"2023","unstructured":"Wang Z, Lim G, Ng WY, Tan T-E, Lim J, Lim SH et al (2023) Synthetic artificial intelligence using generative adversarial network for retinal imaging in detection of age-related macular degeneration. Front Med 10:1184892","journal-title":"Front Med"},{"key":"6830_CR15","doi-asserted-by":"crossref","unstructured":"Greenhalgh T, Thorne S, Malterud K (2018) Time to challenge the spurious hierarchy of systematic over narrative reviews? Eur J Clin Invest 48:e12931","DOI":"10.1111\/eci.12931"},{"key":"6830_CR16","doi-asserted-by":"publisher","first-page":"2815","DOI":"10.1016\/j.bpj.2024.02.019","volume":"123","author":"J Liu","year":"2024","unstructured":"Liu J, Xu S, He P, Wu S, Luo X, Deng Y et al (2024) VSG-GAN: A high-fidelity image synthesis method with semantic manipulation in retinal fundus image. Biophys J 123:2815","journal-title":"Biophys J"},{"issue":"6","key":"6830_CR17","doi-asserted-by":"publisher","first-page":"863","DOI":"10.3390\/healthcare11060863","volume":"11","author":"G Alwakid","year":"2023","unstructured":"Alwakid G, Gouda W, Humayun M (2023) Deep learning-based prediction of diabetic retinopathy using CLAHE and ESRGAN for enhancement. Healthcare 11(6):863","journal-title":"Healthcare"},{"issue":"4","key":"6830_CR18","doi-asserted-by":"publisher","first-page":"100401","DOI":"10.1016\/j.xops.2023.100401","volume":"3","author":"D Shi","year":"2023","unstructured":"Shi D, Zhang W, He S, Chen Y, Song F, Liu S et al (2023) Translation of color fundus photography into fluorescein angiography using deep learning for enhanced diabetic retinopathy screening. Ophthalmol Sci 3(4):100401","journal-title":"Ophthalmol Sci"},{"issue":"12","key":"6830_CR19","doi-asserted-by":"publisher","first-page":"5079","DOI":"10.1007\/s10792-023-02911-y","volume":"43","author":"H Yuan","year":"2023","unstructured":"Yuan H, Dai M, Shi C, Li M, Li H (2023) A generative adversarial neural network with multi-attention feature extraction for fundus lesion segmentation. Int Ophthalmol 43(12):5079\u20135090","journal-title":"Int Ophthalmol"},{"issue":"1","key":"6830_CR20","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/JBHI.2020.3045475","volume":"26","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Wang B, He X, Cui S, Shao L (2022) DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images. IEEE J Biomed Health Inform 26(1):56\u201366","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"6830_CR21","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1097\/APO.0000000000000498","volume":"11","author":"C Zheng","year":"2022","unstructured":"Zheng C, Ye H, Yang J, Fei P, Qiu Y, Xie X et al (2022) Development and clinical validation of semi-supervised Generative adversarial networks for detection of retinal disorders in optical coherence tomography images using small Dataset. Asia-Pac J Ophthalmol 11(3):219\u2013226","journal-title":"Asia-Pac J Ophthalmol"},{"issue":"1","key":"6830_CR22","doi-asserted-by":"publisher","first-page":"3812865","DOI":"10.1155\/2021\/3812865","volume":"2021","author":"Y Chen","year":"2021","unstructured":"Chen Y, Long J, Guo J (2021) RF-GANs: A Method to Synthesize Retinal Fundus Images Based on Generative Adversarial Network. Comput Intell Neurosci 2021(1):3812865","journal-title":"Comput Intell Neurosci"},{"issue":"10","key":"6830_CR23","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.1109\/TMI.2021.3056395","volume":"40","author":"L Ju","year":"2021","unstructured":"Ju L, Wang X, Zhao X, Bonnington P, Drummond T, Ge Z (2021) Leveraging regular fundus images for training UWF fundus diagnosis models via adversarial learning and pseudo-labeling. IEEE Trans Med Imaging 40(10):2911\u20132925","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"6830_CR24","first-page":"2611250","volume":"2021","author":"X Wang","year":"2021","unstructured":"Wang X, Ji Z, Ma X, Zhang Z, Yi Z, Zheng H et al (2021) Automated Grading of diabetic retinopathy with ultra-widefield fluorescein angiography and deep learning. J Diabetes Res 2021(1):2611250","journal-title":"J Diabetes Res"},{"issue":"10","key":"6830_CR25","doi-asserted-by":"publisher","first-page":"4863","DOI":"10.1364\/BOE.9.004863","volume":"9","author":"R Zheng","year":"2018","unstructured":"Zheng R, Liu L, Zhang S, Zheng C, Bunyak F, Xu R et al (2018) Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network. Biomed Opt Express 9(10):4863\u20134878","journal-title":"Biomed Opt Express"},{"issue":"1","key":"6830_CR26","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s41746-024-01018-7","volume":"7","author":"R Chen","year":"2024","unstructured":"Chen R, Zhang W, Song F, Yu H, Cao D, Zheng Y et al (2024) Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening. NPJ Digit Med 7(1):34","journal-title":"NPJ Digit Med"},{"issue":"4","key":"6830_CR27","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.aopr.2023.11.001","volume":"3","author":"F Song","year":"2023","unstructured":"Song F, Zhang W, Zheng Y, Shi D, He M (2023) A deep learning model for generating fundus autofluorescence images from color fundus photography. Adv Ophthalmol Pract Res 3(4):192\u2013198","journal-title":"Adv Ophthalmol Pract Res"},{"issue":"3","key":"6830_CR28","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1001\/jamaophthalmol.2018.6156","volume":"137","author":"PM Burlina","year":"2019","unstructured":"Burlina PM, Joshi N, Pacheco KD, Liu TA, Bressler NM (2019) Assessment of deep generative models for high-resolution synthetic retinal image generation of age-related macular degeneration. JAMA Ophthalmol 137(3):258\u2013264","journal-title":"JAMA Ophthalmol"},{"issue":"20","key":"6830_CR29","doi-asserted-by":"publisher","first-page":"205016","DOI":"10.1088\/1361-6560\/acf3c9","volume":"68","author":"N Hou","year":"2023","unstructured":"Hou N, Shi J, Ding X, Nie C, Wang C, Wan J (2023) ROP-GAN: an image synthesis method for retinopathy of prematurity based on generative adversarial network. Phys Med Biol 68(20):205016","journal-title":"Phys Med Biol"},{"issue":"2","key":"6830_CR30","doi-asserted-by":"publisher","first-page":"100126","DOI":"10.1016\/j.xops.2022.100126","volume":"2","author":"AS Coyner","year":"2022","unstructured":"Coyner AS, Chen JS, Chang K, Singh P, Ostmo S, Chan RP et al (2022) Synthetic medical images for robust, privacy-preserving training of artificial intelligence: application to retinopathy of prematurity diagnosis. Ophthalmol Sci 2(2):100126","journal-title":"Ophthalmol Sci"},{"issue":"7","key":"6830_CR31","doi-asserted-by":"publisher","first-page":"e27822","DOI":"10.2196\/27822","volume":"23","author":"Y Han","year":"2021","unstructured":"Han Y, Li W, Liu M, Wu Z, Zhang F, Liu X et al (2021) Application of an anomaly detection model to screen for ocular diseases using color retinal fundus images: design and evaluation study. J Med Internet Res 23(7):e27822","journal-title":"J Med Internet Res"},{"issue":"6","key":"6830_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1167\/tvst.13.6.1","volume":"13","author":"AK Chaurasia","year":"2024","unstructured":"Chaurasia AK, MacGregor S, Craig JE, Mackey DA, Hewitt AW (2024) Assessing the efficacy of synthetic optic disc images for detecting glaucomatous optic neuropathy using deep learning. Transl Vis Sci Technol 13(6):1","journal-title":"Transl Vis Sci Technol"},{"issue":"12","key":"6830_CR33","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1167\/tvst.12.12.20","volume":"12","author":"S He","year":"2023","unstructured":"He S, Joseph S, Bulloch G, Jiang F, Kasturibai H, Kim R et al (2023) Bridging the camera domain gap with image-to-image translation improves glaucoma diagnosis. Transl Vis Sci Technol 12(12):20","journal-title":"Transl Vis Sci Technol"},{"issue":"10","key":"6830_CR34","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1001\/jamaophthalmol.2022.3375","volume":"140","author":"AJS Kumar","year":"2022","unstructured":"Kumar AJS, Chong RS, Crowston JG, Chua J, Bujor I, Husain R et al (2022) Evaluation of generative adversarial networks for high-resolution synthetic image generation of circumpapillary optical coherence tomography images for glaucoma. JAMA Ophthalmol 140(10):974\u2013981","journal-title":"JAMA Ophthalmol"},{"key":"6830_CR35","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1136\/bjo-2024-325403","volume":"108","author":"L Liu","year":"2024","unstructured":"Liu L, Hong J, Wu Y, Liu S, Wang K, Li M et al (2024) Digital ray: enhancing cataractous fundus images using style transfer generative adversarial networks to improve retinopathy detection. Bri J Ophthalmol 108:1423","journal-title":"Bri J Ophthalmol"},{"issue":"12","key":"6830_CR36","doi-asserted-by":"publisher","first-page":"3374","DOI":"10.1109\/JBHI.2020.2999077","volume":"24","author":"Y Luo","year":"2020","unstructured":"Luo Y, Chen K, Liu L, Liu J, Mao J, Ke G et al (2020) Dehaze of cataractous retinal images using an unpaired generative adversarial network. IEEE J Biomed Health Inform 24(12):3374\u20133383","journal-title":"IEEE J Biomed Health Inform"},{"issue":"5","key":"6830_CR37","doi-asserted-by":"publisher","first-page":"2355","DOI":"10.1364\/BOE.10.002355","volume":"10","author":"H Jiang","year":"2019","unstructured":"Jiang H, Chen X, Shi F, Ma Y, Xiang D, Ye L et al (2019) Improved cGAN based linear lesion segmentation in high myopia ICGA images. Biomed Opt Express 10(5):2355\u20132366","journal-title":"Biomed Opt Express"},{"issue":"7","key":"6830_CR38","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1167\/tvst.13.7.4","volume":"13","author":"J Baek","year":"2024","unstructured":"Baek J, He Y, Emamverdi M, Mahmoudi A, Nittala MG, Corradetti G et al (2024) Prediction of long-term treatment outcomes for diabetic macular edema using a generative adversarial network. Transl Vis Sci Technol 13(7):4","journal-title":"Transl Vis Sci Technol"},{"issue":"1","key":"6830_CR39","doi-asserted-by":"publisher","first-page":"19960","DOI":"10.1038\/s41598-023-46253-2","volume":"13","author":"S Hussain","year":"2023","unstructured":"Hussain S, Chua J, Wong D, Lo J, Kadziauskiene A, Asoklis R et al (2023) Predicting glaucoma progression using deep learning framework guided by generative algorithm. Sci Rep 13(1):19960","journal-title":"Sci Rep"},{"issue":"1","key":"6830_CR40","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1109\/JBHI.2023.3302989","volume":"28","author":"P Zhao","year":"2023","unstructured":"Zhao P, Song X, Xi X, Nie X, Meng X, Qu Y, Yin Y (2023) Biomarkers-Aware Asymmetric Bibranch GAN With Adaptive Memory Batch Normalization for Prediction of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration. IEEE J Biomed Health Inform 28(1):557\u201368","journal-title":"IEEE J Biomed Health Inform"},{"key":"6830_CR41","doi-asserted-by":"publisher","first-page":"107364","DOI":"10.1016\/j.cmpb.2023.107364","volume":"230","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Huang K, Li M, Yuan S, Chen Q (2023) Learn single-horizon disease evolution for predictive generation of post-therapeutic neovascular age-related macular degeneration. Comput Methods Programs Biomed 230:107364","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"6830_CR42","doi-asserted-by":"publisher","first-page":"5639","DOI":"10.1038\/s41598-023-32398-7","volume":"13","author":"S Moon","year":"2023","unstructured":"Moon S, Lee Y, Hwang J, Kim CG, Kim JW, Yoon WT et al (2023) Prediction of anti-vascular endothelial growth factor agent-specific treatment outcomes in neovascular age-related macular degeneration using a generative adversarial network. Sci Rep 13(1):5639","journal-title":"Sci Rep"},{"key":"6830_CR43","doi-asserted-by":"publisher","first-page":"106648","DOI":"10.1016\/j.cmpb.2022.106648","volume":"216","author":"QT Pham","year":"2022","unstructured":"Pham QT, Ahn S, Shin J, Song SJ (2022) Generating future fundus images for early age-related macular degeneration based on generative adversarial networks. Comput Methods Programs Biomed 216:106648","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"6830_CR44","doi-asserted-by":"publisher","first-page":"pgab003","DOI":"10.1093\/pnasnexus\/pgab003","volume":"1","author":"A Ganjdanesh","year":"2022","unstructured":"Ganjdanesh A, Zhang J, Chew EY, Ding Y, Huang H, Chen W (2022) LONGL-Net: temporal correlation structure guided deep learning model to predict longitudinal age-related macular degeneration severity. PNAS nexus. 1(1):pgab003","journal-title":"PNAS nexus."},{"issue":"3","key":"6830_CR45","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1097\/IAE.0000000000002898","volume":"41","author":"H Lee","year":"2021","unstructured":"Lee H, Kim S, Kim MA, Chung H, Kim HC (2021) Post-treatment prediction of optical coherence tomography using a conditional generative adversarial network in age-related macular degeneration. Retina 41(3):572\u2013580","journal-title":"Retina"},{"issue":"12","key":"6830_CR46","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1136\/bjophthalmol-2019-315338","volume":"104","author":"Y Liu","year":"2020","unstructured":"Liu Y, Yang J, Zhou Y, Wang W, Zhao J, Yu W et al (2020) Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network. Br J Ophthalmol 104(12):1735\u20131740","journal-title":"Br J Ophthalmol"},{"key":"6830_CR47","doi-asserted-by":"publisher","first-page":"103628","DOI":"10.1016\/j.compbiomed.2020.103628","volume":"118","author":"TK Yoo","year":"2020","unstructured":"Yoo TK, Choi JY, Kim HK (2020) A generative adversarial network approach to predicting postoperative appearance after orbital decompression surgery for thyroid eye disease. Comput Biol Med 118:103628","journal-title":"Comput Biol Med"},{"issue":"3","key":"6830_CR48","doi-asserted-by":"publisher","first-page":"100169","DOI":"10.1016\/j.xops.2022.100169","volume":"2","author":"Y Sun","year":"2022","unstructured":"Sun Y, Huang X, Zhang Q, Lee SY, Wang Y, Jin K et al (2022) A fully automatic postoperative appearance prediction system for blepharoptosis surgery with image-based deep learning. Ophthalmol Sci 2(3):100169","journal-title":"Ophthalmol Sci"},{"issue":"7","key":"6830_CR49","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1007\/s11548-023-02925-y","volume":"18","author":"Y Frisch","year":"2023","unstructured":"Frisch Y, Fuchs M, Mukhopadhyay A (2023) Temporally consistent sequence-to-sequence translation of cataract surgeries. Int J Comput Assist Radiol Surg 18(7):1217\u20131224","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"10","key":"6830_CR50","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1136\/bjo-2023-324633","volume":"108","author":"JF Assaf","year":"2024","unstructured":"Assaf JF, Abou Mrad A, Reinstein DZ, Amescua G, Zakka C, Archer TJ et al (2024) Creating realistic anterior segment optical coherence tomography images using generative adversarial networks. Bri J Ophthalmol 108(10):1414","journal-title":"Bri J Ophthalmol"},{"issue":"5","key":"6830_CR51","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1001\/jamaophthalmol.2014.84","volume":"132","author":"A Sommer","year":"2014","unstructured":"Sommer A, Taylor HR, Ravilla TD, West S, Lietman TM, Keenan JD et al (2014) Challenges of ophthalmic care in the developing world. JAMA Ophthalmol 132(5):640\u2013644","journal-title":"JAMA Ophthalmol"},{"issue":"1","key":"6830_CR52","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/s41433-023-02649-y","volume":"38","author":"E Waisberg","year":"2024","unstructured":"Waisberg E, Ong J, Zaman N, Kamran SA, Sarker P, Tavakkoli A et al (2024) Extended reality for strabismus screening in developing countries. Eye. 38(1):224","journal-title":"Eye."},{"key":"6830_CR53","doi-asserted-by":"publisher","first-page":"255","DOI":"10.2147\/EB.S272343","volume":"13","author":"E Waisberg","year":"2021","unstructured":"Waisberg E, Micieli JA (2021) Neuro-ophthalmological optic nerve cupping: an overview. Eye Brain 13:255\u201368","journal-title":"Eye Brain"},{"issue":"11","key":"6830_CR54","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","volume":"121","author":"Y-C Tham","year":"2014","unstructured":"Tham Y-C, Li X, Wong TY, Quigley HA, Aung T, Cheng C-Y (2014) Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmol 121(11):2081\u20132090","journal-title":"Ophthalmol"},{"issue":"4","key":"6830_CR55","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.3390\/encyclopedia3040086","volume":"3","author":"M Masalkhi","year":"2023","unstructured":"Masalkhi M, Ong J, Waisberg E, Berdahl J, Lee AG (2023) Intraocular pressure during spaceflight and risk of glaucomatous damage in prolonged microgravity. Encyclopedia 3(4):1187\u20131196","journal-title":"Encyclopedia"},{"issue":"12","key":"6830_CR56","doi-asserted-by":"publisher","first-page":"1740","DOI":"10.1001\/archopht.126.12.1740","volume":"126","author":"JB Saaddine","year":"2008","unstructured":"Saaddine JB, Honeycutt AA, Narayan KV, Zhang X, Klein R, Boyle JP (2008) Projection of diabetic retinopathy and other major eye diseases among people with diabetes mellitus: United States, 2005\u20132050. Arch Ophthalmol 126(12):1740\u20131747","journal-title":"Arch Ophthalmol"},{"issue":"5","key":"6830_CR57","first-page":"75","volume":"107","author":"AN Kollias","year":"2010","unstructured":"Kollias AN, Ulbig MW (2010) Diabetic retinopathy: early diagnosis and effective treatment. Deutsches Arzteblatt International 107(5):75","journal-title":"Deutsches Arzteblatt International"},{"issue":"1","key":"6830_CR58","doi-asserted-by":"publisher","first-page":"e001140","DOI":"10.1136\/bmjophth-2022-001140","volume":"7","author":"Y Rom","year":"2022","unstructured":"Rom Y, Aviv R, Ianchulev T, Dvey-Aharon Z (2022) Predicting the future development of diabetic retinopathy using a deep learning algorithm for the analysis of non-invasive retinal imaging. BMJ Open Ophthalmol 7(1):e001140","journal-title":"BMJ Open Ophthalmol"},{"issue":"9","key":"6830_CR59","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.2337\/diacare.26.9.2653","volume":"26","author":"TA Ciulla","year":"2003","unstructured":"Ciulla TA, Amador AG, Zinman B (2003) Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies. Diabetes Care 26(9):2653\u20132664","journal-title":"Diabetes Care"},{"key":"6830_CR60","unstructured":"Hageman GS, Gehrs K, Johnson LV, Anderson D (2008) Age-related macular degeneration (AMD). In: Kolb H, Fernandez E, Jones B et al (eds) Webvision: the organization of the retina and visual system [Internet]. Salt Lake City (UT): University of Utah Health Sciences Center. Available from: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK11530\/"},{"issue":"1","key":"6830_CR61","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41572-021-00265-2","volume":"7","author":"M Fleckenstein","year":"2021","unstructured":"Fleckenstein M, Keenan TD, Guymer RH, Chakravarthy U, Schmitz-Valckenberg S, Klaver CC et al (2021) Age-related macular degeneration. Nat Rev Dis Primers 7(1):31","journal-title":"Nat Rev Dis Primers"},{"issue":"2","key":"6830_CR62","doi-asserted-by":"publisher","first-page":"e106","DOI":"10.1016\/S2214-109X(13)70145-1","volume":"2","author":"WL Wong","year":"2014","unstructured":"Wong WL, Su X, Li X, Cheung CMG, Klein R, Cheng C-Y et al (2014) Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob Health 2(2):e106\u2013e116","journal-title":"Lancet Glob Health"},{"key":"6830_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40662-014-0009-8","volume":"1","author":"C McAlinden","year":"2014","unstructured":"McAlinden C (2014) An overview of thyroid eye disease. Eye and Vision 1:1\u20134","journal-title":"Eye and Vision"},{"issue":"1","key":"6830_CR64","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1542\/peds.2004-1413","volume":"116","author":"W Good","year":"2005","unstructured":"Good W, Hardy R, Dobson V, Group ETfRoPC et al (2005) The incidence and course of retinopathy of prematurity: findings from the early treatment for retinopathy of prematurity study. Pediatrics 116(1):15\u201323","journal-title":"Pediatrics"},{"issue":"1","key":"6830_CR65","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1038\/pr.2013.205","volume":"74","author":"H Blencowe","year":"2013","unstructured":"Blencowe H, Lawn JE, Vazquez T, Fielder A, Gilbert C (2013) Preterm-associated visual impairment and estimates of retinopathy of prematurity at regional and global levels for 2010. Pediatr Res 74(1):35\u201349","journal-title":"Pediatr Res"},{"issue":"3","key":"6830_CR66","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1080\/01676830.2017.1279669","volume":"36","author":"HS Richards","year":"2017","unstructured":"Richards HS, Jenkinson E, Rumsey N, Harrad RA (2017) Pre-operative experiences and post-operative benefits of ptosis surgery: a qualitative study. Orbit 36(3):147\u2013153","journal-title":"Orbit"},{"issue":"6","key":"6830_CR67","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1016\/j.ajo.2007.02.022","volume":"143","author":"R McKean-Cowdin","year":"2007","unstructured":"McKean-Cowdin R, Varma R, Wu J, Hays RD, Azen SP, Group LALES (2007) Severity of visual field loss and health-related quality of life. Am J Ophthalmol 143(6):1013\u201323","journal-title":"Am J Ophthalmol"},{"issue":"7","key":"6830_CR68","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.jfo.2019.11.009","volume":"43","author":"M Delbarre","year":"2020","unstructured":"Delbarre M, Froussart-Maille F (2020) S\u00e9miologie et formes cliniques de la cataracte chez l\u2019adulte. J Fr Ophtalmol 43(7):653\u2013659","journal-title":"J Fr Ophtalmol"},{"key":"6830_CR69","first-page":"601","volume-title":"Global initiative for the elimination of avoidable blindness","author":"R Lindfield","year":"2012","unstructured":"Lindfield R, Kocur I, Limburg H, Foster A (2012) Global initiative for the elimination of avoidable blindness. World Scientific, The Epidemiology of Eye Disease, pp 601\u2013606"},{"issue":"9459","key":"6830_CR70","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1016\/S0140-6736(05)70803-5","volume":"365","author":"PA Asbell","year":"2005","unstructured":"Asbell PA, Dualan I, Mindel J, Brocks D, Ahmad M, Epstein S (2005) Age-related cataract. The Lancet 365(9459):599\u2013609","journal-title":"The Lancet"},{"key":"6830_CR71","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1136\/bjo.85.6.635","volume":"85","author":"A Foster","year":"2001","unstructured":"Foster A (2001) Cataract and \u201cVision 2020\u2014the right to sight\u201d initiative. Br J Ophthalmol 85:635\u2013637","journal-title":"Br J Ophthalmol"},{"issue":"1","key":"6830_CR72","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1001\/archopht.125.1.38","volume":"125","author":"TL Young","year":"2007","unstructured":"Young TL, Metlapally R, Shay AE (2007) Complex trait genetics of refractive error. Arch Ophthalmol 125(1):38\u201348","journal-title":"Arch Ophthalmol"},{"issue":"1","key":"6830_CR73","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1038\/s41572-020-00231-4","volume":"6","author":"PN Baird","year":"2020","unstructured":"Baird PN, Saw S-M, Lanca C, Guggenheim JA, Smith EL III, Zhou X et al (2020) Myopia. Nat Rev Dis Prim 6(1):99","journal-title":"Nat Rev Dis Prim"},{"issue":"5","key":"6830_CR74","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1016\/j.ophtha.2016.01.006","volume":"123","author":"BA Holden","year":"2016","unstructured":"Holden BA, Fricke TR, Wilson DA, Jong M, Naidoo KS, Sankaridurg P et al (2016) Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050. Ophthalmology 123(5):1036\u20131042","journal-title":"Ophthalmology"},{"issue":"12","key":"6830_CR75","doi-asserted-by":"publisher","first-page":"2580","DOI":"10.1038\/s41433-022-02353-3","volume":"37","author":"E Waisberg","year":"2023","unstructured":"Waisberg E, Ong J, Paladugu P, Kamran SA, Zaman N, Tavakkoli A et al (2023) Applying generative adversarial network techniques to portable ophthalmic imaging. Eye 37(12):2580\u20132581","journal-title":"Eye"},{"issue":"10","key":"6830_CR76","doi-asserted-by":"publisher","first-page":"2130","DOI":"10.1007\/s10439-023-03304-z","volume":"51","author":"PS Paladugu","year":"2023","unstructured":"Paladugu PS, Ong J, Nelson N, Kamran SA, Waisberg E, Zaman N et al (2023) Generative adversarial networks in medicine: important considerations for this emerging innovation in artificial intelligence. Ann Biomed Eng 51(10):2130\u20132142","journal-title":"Ann Biomed Eng"},{"key":"6830_CR77","doi-asserted-by":"publisher","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv:14111784 [cs.LG]. https:\/\/doi.org\/10.48550\/arXiv.1411.1784","DOI":"10.48550\/arXiv.1411.1784"},{"key":"6830_CR78","doi-asserted-by":"publisher","unstructured":"Karras T, Aila T, Laine S, Lehtinen J (2017) Progressive growing of gans for improved quality, stability, and variation. arXiv:1710.10196v3 [cs.NE]. https:\/\/doi.org\/10.48550\/arXiv.1710.10196","DOI":"10.48550\/arXiv.1710.10196"},{"key":"6830_CR79","doi-asserted-by":"publisher","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434v2 [cs.LG]. https:\/\/doi.org\/10.48550\/arXiv.1511.06434","DOI":"10.48550\/arXiv.1511.06434"},{"issue":"7","key":"6830_CR80","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1136\/bjo-2022-322892","volume":"107","author":"J Ong","year":"2023","unstructured":"Ong J, Tarver W, Brunstetter T, Mader TH, Gibson CR, Mason SS et al (2023) Spaceflight associated neuro-ocular syndrome: proposed pathogenesis, terrestrial analogues, and emerging countermeasures. Br J Ophthalmol 107(7):895\u2013900","journal-title":"Br J Ophthalmol"},{"key":"6830_CR81","first-page":"36","volume":"36","author":"E Waisberg","year":"2023","unstructured":"Waisberg E, Ong J, Kamran SA, Paladugu P, Zaman N, Lee AG et al (2023) Transfer learning as an AI-based solution to address limited datasets in space medicine. Life Sci Space Res 36:36\u201338","journal-title":"Life Sci Space Res"},{"issue":"1","key":"6830_CR82","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s40662-022-00277-3","volume":"9","author":"A You","year":"2022","unstructured":"You A, Kim JK, Ryu IH, Yoo TK (2022) Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye Vis 9(1):6","journal-title":"Eye Vis"},{"issue":"6","key":"6830_CR83","doi-asserted-by":"publisher","first-page":"568","DOI":"10.3390\/bioengineering11060568","volume":"11","author":"KG Lee","year":"2024","unstructured":"Lee KG, Song SJ, Lee S, Kim BH, Kong M, Lee KM (2024) FQ-UWF: Unpaired Generative Image Enhancement for Fundus Quality Ultra-Widefield Retinal Images. Bioengineering 11(6):568","journal-title":"Bioengineering"},{"issue":"7","key":"6830_CR84","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1038\/s41433-023-02877-2","volume":"38","author":"M Masalkhi","year":"2024","unstructured":"Masalkhi M, Ong J, Waisberg E, Lee AG (2024) Deep learning in ophthalmic and orbital ultrasound for spaceflight associated neuro-ocular syndrome (SANS). Eye. 38(7):1397","journal-title":"Eye."},{"issue":"4","key":"6830_CR85","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1111\/ceo.12696","volume":"44","author":"DSW Ting","year":"2016","unstructured":"Ting DSW, Cheung GCM, Wong TY (2016) Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review. Clin Exp Ophthalmol 44(4):260\u2013277","journal-title":"Clin Exp Ophthalmol"},{"key":"6830_CR86","doi-asserted-by":"crossref","unstructured":"Jeon M, Park H, Kim HJ, Morley M, Cho H (eds) k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment. In: Computer Vision - ECCV 2022: 17th European Conference, Proceedings, Part XXI, Springer,\u00a0pp 661\u2013678","DOI":"10.1007\/978-3-031-19803-8_39"},{"issue":"1","key":"6830_CR87","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/bioengineering11010004","volume":"11","author":"A Sebastian","year":"2023","unstructured":"Sebastian A, Elharrouss O, Al-Maadeed S, Almaadeed N (2023) GAN-based approach for diabetic retinopathy retinal vasculature segmentation. Bioengineering 11(1):4","journal-title":"Bioengineering"},{"issue":"11","key":"6830_CR88","doi-asserted-by":"publisher","first-page":"7357","DOI":"10.1002\/mp.15988","volume":"49","author":"M Zhao","year":"2022","unstructured":"Zhao M, Lu Z, Zhu S, Wang X, Feng J (2022) Automatic generation of retinal optical coherence tomography images based on generative adversarial networks. Med Phys 49(11):7357\u20137367","journal-title":"Med Phys"},{"issue":"9","key":"6830_CR89","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1109\/TMI.2019.2903434","volume":"38","author":"A Diaz-Pinto","year":"2019","unstructured":"Diaz-Pinto A, Colomer A, Naranjo V, Morales S, Xu Y, Frangi AF (2019) Retinal image synthesis and semi-supervised learning for glaucoma assessment. IEEE Trans Med Imaging 38(9):2211\u20132218","journal-title":"IEEE Trans Med Imaging"},{"key":"6830_CR90","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s00417-017-3833-4","volume":"256","author":"K-C Hung","year":"2018","unstructured":"Hung K-C, Chen M-S, Yang C-M, Wang S-W, Ho T-C (2018) Multimodal imaging of linear lesions in the fundus of pathologic myopic eyes with macular lesions. Graefes Arch Clin Exp Ophthalmol 256:71\u201381","journal-title":"Graefes Arch Clin Exp Ophthalmol"},{"issue":"3","key":"6830_CR91","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1097\/APO.0000000000000468","volume":"11","author":"VW Hui","year":"2022","unstructured":"Hui VW, Szeto SK, Tang F, Yang D, Chen H, Lai TY et al (2022) Optical coherence tomography classification systems for Diabetic Macular Edema and their associations with visual outcome and treatment responses\u2013an updated review. Asia-Pac J Ophthalmol 11(3):247\u2013257","journal-title":"Asia-Pac J Ophthalmol"},{"issue":"5","key":"6830_CR92","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0039-6257(05)80092-X","volume":"39","author":"AC Bird","year":"1995","unstructured":"Bird AC, Bressler NM, Bressler SB, Chisholm IH, Coscas G, Davis MD et al (1995) An international classification and grading system for age-related maculopathy and age-related macular degeneration. Surv Ophthalmol 39(5):367\u2013374","journal-title":"Surv Ophthalmol"},{"issue":"3","key":"6830_CR93","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.ajo.2006.11.041","volume":"143","author":"FG Holz","year":"2007","unstructured":"Holz FG, Bindewald-Wittich A, Fleckenstein M, Dreyhaupt J, Scholl HP, Schmitz-Valckenberg S et al (2007) Progression of geographic atrophy and impact of fundus autofluorescence patterns in age-related macular degeneration. Am J Ophthalmol 143(3):463-722.e2","journal-title":"Am J Ophthalmol"},{"issue":"6","key":"6830_CR94","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.17219\/acem\/27093","volume":"24","author":"HM Zaj\u0105c-Pytrus","year":"2015","unstructured":"Zaj\u0105c-Pytrus HM, Pilecka A, Turno-Kr\u0119cicka A, Adamiec-Mroczek J, Misiuk-Hoj\u0142o M (2015) The dry form of age-related macular degeneration (AMD): the current concepts of pathogenesis and prospects for treatment. Adv Clin Exp Med 24(6):1099\u20131104","journal-title":"Adv Clin Exp Med"},{"key":"6830_CR95","doi-asserted-by":"crossref","unstructured":"Solomon SD, Lindsley K, Vedula SS, Krzystolik MG, Hawkins BS (2014) Anti\u2010vascular endothelial growth factor for neovascular age\u2010related macular degeneration. Cochrane Database Syst Rev 8(8):CD005139","DOI":"10.1002\/14651858.CD005139.pub3"},{"issue":"9","key":"6830_CR96","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1136\/bjophthalmol-2014-305702","volume":"98","author":"U Schmidt-Erfurth","year":"2014","unstructured":"Schmidt-Erfurth U, Chong V, Loewenstein A, Larsen M, Souied E, Schlingemann R et al (2014) Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA). Br J Ophthalmol 98(9):1144\u20131167","journal-title":"Br J Ophthalmol"},{"issue":"6","key":"6830_CR97","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1038\/eye.2015.48","volume":"29","author":"W Amoaku","year":"2015","unstructured":"Amoaku W, Chakravarthy U, Gale R, Gavin M, Ghanchi F, Gibson J et al (2015) Defining response to anti-VEGF therapies in neovascular AMD. Eye 29(6):721\u2013731","journal-title":"Eye"},{"issue":"3","key":"6830_CR98","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1080\/17469899.2016.1180246","volume":"11","author":"LJ Saunders","year":"2016","unstructured":"Saunders LJ, Medeiros FA, Weinreb RN, Zangwill LM (2016) What rates of glaucoma progression are clinically significant? Exp Rev Ophthalmol 11(3):227\u2013234","journal-title":"Exp Rev Ophthalmol"},{"issue":"14","key":"6830_CR99","doi-asserted-by":"publisher","first-page":"5872","DOI":"10.1167\/iovs.16-19894","volume":"57","author":"W Wang","year":"2016","unstructured":"Wang W, Yan W, Fotis K, Prasad NM, Lansingh VC, Taylor HR et al (2016) Cataract surgical rate and socioeconomics: a global study. Invest Ophthalmol Vis Sci 57(14):5872\u20135881","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"6","key":"6830_CR100","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1016\/S0161-6420(94)31210-3","volume":"101","author":"EP Steinberg","year":"1994","unstructured":"Steinberg EP, Tielsch JM, Schein OD, Javitt JC, Sharkey P, Cassard SD et al (1994) National study of cataract surgery outcomes: variation in 4-month postoperative outcomes as reflected in multiple outcome measures. Ophthalmology 101(6):1131\u20131141","journal-title":"Ophthalmology"},{"key":"6830_CR101","doi-asserted-by":"publisher","first-page":"969","DOI":"10.2147\/OPTH.S379856","volume":"17","author":"T Wannapanich","year":"2023","unstructured":"Wannapanich T, Kasetsuwan N, Reinprayoon U (2023) Intraocular implantable collamer lens with a central hole implantation: safety, efficacy, and patient outcomes. Clin Ophthalmol 17:969\u201380","journal-title":"Clin Ophthalmol"},{"issue":"4","key":"6830_CR102","doi-asserted-by":"publisher","first-page":"e199","DOI":"10.3928\/1081597X-20240214-01","volume":"40","author":"JF Assaf","year":"2024","unstructured":"Assaf JF, Yazbeck H, Reinstein DZ, Archer TJ, Arbelaez J, Bteich Y et al (2024) Enhancing the automated detection of implantable collamer lens vault using generative adversarial networks and synthetic data on optical coherence tomography. J Refract Surg 40(4):e199\u2013e207","journal-title":"J Refract Surg"},{"key":"6830_CR103","doi-asserted-by":"publisher","first-page":"3977","DOI":"10.1109\/ACCESS.2018.2789526","volume":"6","author":"AR Dhaini","year":"2018","unstructured":"Dhaini AR, Chokr M, El-Oud SM, Fattah MA, Awwad S (2018) Automated detection and measurement of corneal haze and demarcation line in spectral-domain optical coherence tomography images. IEEE Access 6:3977\u20133991","journal-title":"IEEE Access"},{"issue":"3","key":"6830_CR104","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1111\/j.1365-2265.2005.02345.x","volume":"63","author":"I Wakelkamp","year":"2005","unstructured":"Wakelkamp I, Baldeschi L, Saeed P, Mourits M, Prummel M, Wiersinga W (2005) Surgical or medical decompression as a first-line treatment of optic neuropathy in Graves\u2019 ophthalmopathy? A randomized controlled trial. Clin Endocrinol 63(3):323\u2013328","journal-title":"Clin Endocrinol"},{"issue":"3","key":"6830_CR105","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1038\/eye.2017.260","volume":"32","author":"J Jefferis","year":"2018","unstructured":"Jefferis J, Jones R, Currie Z, Tan J, Salvi S (2018) Orbital decompression for thyroid eye disease: methods, outcomes, and complications. Eye 32(3):626\u2013636","journal-title":"Eye"},{"issue":"3","key":"6830_CR106","doi-asserted-by":"publisher","first-page":"3185193","DOI":"10.21037\/apm-21-345","volume":"10","author":"H Cai","year":"2021","unstructured":"Cai H, Xu S (2021) A randomized trial of psychological intervention on perioperative anxiety and depression of patients with severe blepharoptosis undergoing autologous fascia lata frontal muscle suspension. Ann Palliat Med 10(3):3185193\u20133193193","journal-title":"Ann Palliat Med"},{"issue":"1","key":"6830_CR107","doi-asserted-by":"publisher","first-page":"6859","DOI":"10.1038\/s41467-024-50911-y","volume":"15","author":"EE Brown","year":"2024","unstructured":"Brown EE, Guy AA, Holroyd NA, Sweeney PW, Gourmet L, Coleman H et al (2024) Physics-informed deep generative learning for quantitative assessment of the retina. Nat Commun 15(1):6859","journal-title":"Nat Commun"},{"key":"6830_CR108","doi-asserted-by":"crossref","unstructured":"Menten MJ, Paetzold JC, Dima A, Menze BH, Knier B, Rueckert D (eds) (2022) Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of oct angiographs. In: Wang L, Dou Q, Fletcher PT, Speidel S, Li S (eds) Medical image computing and computer assisted intervention \u2013 MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13438. Springer, Cham","DOI":"10.1007\/978-3-031-16452-1_32"}],"container-title":["Graefe's Archive for Clinical and Experimental Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00417-025-06830-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00417-025-06830-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00417-025-06830-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T19:44:17Z","timestamp":1757187857000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00417-025-06830-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":108,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["6830"],"URL":"https:\/\/doi.org\/10.1007\/s00417-025-06830-9","relation":{},"ISSN":["0721-832X","1435-702X"],"issn-type":[{"value":"0721-832X","type":"print"},{"value":"1435-702X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"21 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosures"}},{"value":"We declare that we have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}