{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:35:00Z","timestamp":1790271300503,"version":"4.1.0"},"reference-count":193,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001778","name":"Deakin University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001778","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Diabetes Metab Disord"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diabetes mellitus (DM) increases the risk of vascular complications, and retinal vasculature imaging serves as a valuable indicator of both microvascular and macrovascular health. Moreover, artificial intelligence (AI)-enabled systems developed for high-throughput detection of diabetic retinopathy (DR) using digitized retinal images have become clinically adopted. This study reviews AI applications using retinal images for DM-related complications, highlighting advancements beyond DR screening, diagnosis, and prognosis, and addresses implementation challenges, such as ethics, data privacy, equitable access, and explainability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a thorough literature search across several databases, including PubMed, Scopus, and Web of Science, focusing on studies involving diabetes, the retina, and artificial intelligence. We reviewed the original research based on their methodology, AI algorithms, data processing techniques, and validation procedures to ensure a detailed analysis of AI applications in diabetic retinal imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Retinal images can be used to diagnose DM complications including DR, neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as to predict the risk of cardiovascular events. Beyond DR screening, AI integration also offers significant potential to address the challenges in the comprehensive care of patients with DM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>With the ability to evaluate the patient\u2019s health status in relation to DM complications as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s40200-025-01596-7","type":"journal-article","created":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T17:28:09Z","timestamp":1744478889000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0663-850X","authenticated-orcid":false,"given":"Navid","family":"Sobhi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2175-1316","authenticated-orcid":false,"given":"Yasin","family":"Sadeghi-Bazargani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3573-6985","authenticated-orcid":false,"given":"Majid","family":"Mirzaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2444-1172","authenticated-orcid":false,"given":"Mirsaeed","family":"Abdollahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4922-1923","authenticated-orcid":false,"given":"Ali","family":"Jafarizadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2974-1801","authenticated-orcid":false,"given":"Siamak","family":"Pedrammehr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0898-5054","authenticated-orcid":false,"given":"Roohallah","family":"Alizadehsani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2086-6517","authenticated-orcid":false,"given":"Ru-San","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7926-9368","authenticated-orcid":false,"given":"Sheikh Mohammed Shariful","family":"Islam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2689-8552","authenticated-orcid":false,"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,12]]},"reference":[{"issue":"4","key":"1596_CR1","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s11886-019-1107-y","volume":"21","author":"D Glovaci","year":"2019","unstructured":"Glovaci D, Fan W, Wong ND. Epidemiology of diabetes mellitus and cardiovascular disease. Curr Cardiol Rep. 2019;21(4):21. https:\/\/doi.org\/10.1007\/s11886-019-1107-y.","journal-title":"Curr Cardiol Rep"},{"key":"1596_CR2","doi-asserted-by":"crossref","unstructured":"Arokiasamy P, Salvi S, Selvamani Y. Global burden of diabetes mellitus. Handbook of global health. Springer; 2021. pp. 1\u201344.","DOI":"10.1007\/978-3-030-05325-3_28-2"},{"issue":"9368","key":"1596_CR3","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1016\/S0140-6736(03)13169-8","volume":"361","author":"WJ Jeffcoate","year":"2003","unstructured":"Jeffcoate WJ, Harding KG. Diabetic foot ulcers. Lancet. 2003;361(9368):1545\u201351. https:\/\/doi.org\/10.1016\/S0140-6736(03)13169-8.","journal-title":"Lancet"},{"issue":"2","key":"1596_CR4","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1053\/j.ackd.2017.10.011","volume":"25","author":"DN Koye","year":"2018","unstructured":"Koye DN, Magliano DJ, Nelson RG, Pavkov ME. The global epidemiology of diabetes and kidney disease. Adv Chronic Kidney Dis. 2018;25(2):121\u201332. https:\/\/doi.org\/10.1053\/j.ackd.2017.10.011.","journal-title":"Adv Chronic Kidney Dis"},{"issue":"9735","key":"1596_CR5","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/s0140-6736(09)62124-3","volume":"376","author":"N Cheung","year":"2010","unstructured":"Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet. 2010;376(9735):124\u201336. https:\/\/doi.org\/10.1016\/s0140-6736(09)62124-3.","journal-title":"Lancet"},{"issue":"3","key":"1596_CR6","doi-asserted-by":"publisher","first-page":"556","DOI":"10.2337\/dc11-1909","volume":"35","author":"JW Yau","year":"2012","unstructured":"Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35(3):556\u201364. https:\/\/doi.org\/10.2337\/dc11-1909.","journal-title":"Diabetes Care"},{"issue":"3","key":"1596_CR7","doi-asserted-by":"publisher","first-page":"179","DOI":"10.4103\/0301-4738.40355","volume":"56","author":"R Singh","year":"2008","unstructured":"Singh R, Ramasamy K, Abraham C, Gupta V, Gupta A. Diabetic retinopathy: an update. Indian J Ophthalmol. 2008;56(3):179\u201388.","journal-title":"Indian J Ophthalmol"},{"issue":"6","key":"1596_CR8","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1016\/S0002-9394(99)00033-1","volume":"127","author":"T Otani","year":"1999","unstructured":"Otani T, Kishi S, Maruyama Y. Patterns of diabetic macular edema with optical coherence tomography. Am J Ophthalmol. 1999;127(6):688\u201393.","journal-title":"Am J Ophthalmol"},{"key":"1596_CR9","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/0039-6257(84)90233-9","volume":"28","author":"M Yanoff","year":"1984","unstructured":"Yanoff M, Fine BS, Brucker AJ, Eagle RC Jr. Pathology of human cystoid macular edema. Surv Ophthalmol. 1984;28:505\u201311.","journal-title":"Surv Ophthalmol"},{"issue":"3","key":"1596_CR10","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1001\/jamaophthalmol.2013.1743","volume":"131","author":"MD Abr\u00e0moff","year":"2013","unstructured":"Abr\u00e0moff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 2013;131(3):351\u20137. https:\/\/doi.org\/10.1001\/jamaophthalmol.2013.1743","journal-title":"JAMA Ophthalmol"},{"issue":"5","key":"1596_CR11","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3238\/arztebl.2010.0075","volume":"107","author":"AN Kollias","year":"2010","unstructured":"Kollias AN, Ulbig MW. Diabetic retinopathy: early diagnosis and effective treatment. Dtsch Arztebl Int. 2010;107(5):75\u201383. https:\/\/doi.org\/10.3238\/arztebl.2010.0075. quiz 4.","journal-title":"Dtsch Arztebl Int"},{"issue":"1","key":"1596_CR12","first-page":"3","volume":"1","author":"DE McMillan","year":"1984","unstructured":"McMillan DE. The microcirculation in diabetes. Microcirc Endothelium Lymphatics. 1984;1(1):3\u201324.","journal-title":"Microcirc Endothelium Lymphatics"},{"key":"1596_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11195546","author":"K Abbas","year":"2022","unstructured":"Abbas K, Lu Y, Bavishi S, Mishra N, TomThundyil S, Sawant SA, et al. A simple review of small vessel disease manifestation in the brain, retina, and kidneys. J Clin Med. 2022. https:\/\/doi.org\/10.3390\/jcm11195546.","journal-title":"J Clin Med"},{"issue":"3","key":"1596_CR14","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1097\/cm9.0000000000001816","volume":"135","author":"S Li","year":"2021","unstructured":"Li S, Zhao R, Zou H. Artificial intelligence for diabetic retinopathy. Chin Med J (Engl). 2021;135(3):253\u201360. https:\/\/doi.org\/10.1097\/cm9.0000000000001816.","journal-title":"Chin Med J (Engl)"},{"issue":"10","key":"1596_CR15","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.2337\/dci23-0032","volume":"46","author":"AE Rajesh","year":"2023","unstructured":"Rajesh AE, Davidson OQ, Lee CS, Lee AY. Artificial intelligence and diabetic retinopathy: AI framework, prospective studies, Head-to-head validation, and Cost-effectiveness. Diabetes Care. 2023;46(10):1728\u201339. https:\/\/doi.org\/10.2337\/dci23-0032.","journal-title":"Diabetes Care"},{"key":"1596_CR16","doi-asserted-by":"publisher","first-page":"2728719","DOI":"10.1155\/2023\/2728719","volume":"2023","author":"P Uppamma","year":"2023","unstructured":"Uppamma P, Bhattacharya S. Deep learning and medical image processing techniques for diabetic retinopathy: A survey of applications, challenges, and future trends. J Healthc Eng. 2023;2023:2728719. https:\/\/doi.org\/10.1155\/2023\/2728719.","journal-title":"J Healthc Eng"},{"issue":"3","key":"1596_CR17","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1038\/s41433-023-02717-3","volume":"38","author":"MB Gon\u00e7alves","year":"2024","unstructured":"Gon\u00e7alves MB, Nakayama LF, Ferraz D, Faber H, Korot E, Malerbi FK, et al. Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review. Eye (Lond). 2024;38(3):426\u201333. https:\/\/doi.org\/10.1038\/s41433-023-02717-3.","journal-title":"Eye (Lond)"},{"issue":"3","key":"1596_CR18","doi-asserted-by":"publisher","first-page":"182","DOI":"10.2174\/1573399811666150330150038","volume":"11","author":"U Balakrishnan","year":"2015","unstructured":"Balakrishnan U, Venkatachalapathy K, Marimuthu GS. A hybrid PSO-DEFS based feature selection for the identification of diabetic retinopathy. Curr Diabetes Rev. 2015;11(3):182\u201390. https:\/\/doi.org\/10.2174\/1573399811666150330150038.","journal-title":"Curr Diabetes Rev"},{"issue":"11","key":"1596_CR19","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/s11892-017-0940-x","volume":"17","author":"LI Mudie","year":"2017","unstructured":"Mudie LI, Wang X, Friedman DS, Brady CJ. Crowdsourcing and automated retinal image analysis for diabetic retinopathy. Curr Diab Rep. 2017;17(11):106. https:\/\/doi.org\/10.1007\/s11892-017-0940-x.","journal-title":"Curr Diab Rep"},{"issue":"1","key":"1596_CR20","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1038\/s41433-018-0269-y","volume":"33","author":"R Raman","year":"2019","unstructured":"Raman R, Srinivasan S, Virmani S, Sivaprasad S, Rao C, Rajalakshmi R. Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy. Eye (Lond). 2019;33(1):97\u2013109. https:\/\/doi.org\/10.1038\/s41433-018-0269-y.","journal-title":"Eye (Lond)"},{"key":"1596_CR21","doi-asserted-by":"publisher","first-page":"101701","DOI":"10.1016\/j.artmed.2019.07.009","volume":"99","author":"N Asiri","year":"2019","unstructured":"Asiri N, Hussain M, Al Adel F, Alzaidi N. Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artif Intell Med. 2019;99:101701. https:\/\/doi.org\/10.1016\/j.artmed.2019.07.009.","journal-title":"Artif Intell Med"},{"issue":"1","key":"1596_CR22","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1038\/s41433-020-01262-7","volume":"35","author":"R Rajalakshmi","year":"2021","unstructured":"Rajalakshmi R, Prathiba V, Arulmalar S, Usha M. Review of retinal cameras for global coverage of diabetic retinopathy screening. Eye (Lond). 2021;35(1):162\u201372. https:\/\/doi.org\/10.1038\/s41433-020-01262-7.","journal-title":"Eye (Lond)"},{"issue":"1","key":"1596_CR23","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.survophthal.2022.08.004","volume":"68","author":"MH Shahriari","year":"2023","unstructured":"Shahriari MH, Sabbaghi H, Asadi F, Hosseini A, Khorrami Z. Artificial intelligence in screening, diagnosis, and classification of diabetic macular edema: A systematic review. Surv Ophthalmol. 2023;68(1):42\u201353. https:\/\/doi.org\/10.1016\/j.survophthal.2022.08.004.","journal-title":"Surv Ophthalmol"},{"key":"1596_CR24","doi-asserted-by":"publisher","first-page":"1079217","DOI":"10.3389\/fendo.2022.1079217","volume":"13","author":"Z Yang","year":"2022","unstructured":"Yang Z, Tan TE, Shao Y, Wong TY, Li X. Classification of diabetic retinopathy: past, present and future. Front Endocrinol (Lausanne). 2022;13:1079217. https:\/\/doi.org\/10.3389\/fendo.2022.1079217.","journal-title":"Front Endocrinol (Lausanne)"},{"key":"1596_CR25","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.16-19964","author":"MD Abr\u00e0moff","year":"2016","unstructured":"Abr\u00e0moff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200\u201306. https:\/\/doi.org\/10.1167\/iovs.16-19964"},{"issue":"3","key":"1596_CR26","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.ophtha.2016.11.014","volume":"124","author":"A Tufail","year":"2017","unstructured":"Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, et al. Automated diabetic retinopathy image assessment software: diagnostic accuracy and Cost-Effectiveness compared with human graders. Ophthalmology. 2017;124(3):343\u201351. https:\/\/doi.org\/10.1016\/j.ophtha.2016.11.014.","journal-title":"Ophthalmology"},{"issue":"5","key":"1596_CR27","doi-asserted-by":"publisher","first-page":"e240","DOI":"10.1016\/S2589-7500(20)30060-1","volume":"2","author":"Y Xie","year":"2020","unstructured":"Xie Y, Nguyen QD, Hamzah H, Lim G, Bellemo V, Gunasekeran DV, et al. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a National programme: an economic analysis modelling study. Lancet Digit Health. 2020;2(5):e240\u20139. https:\/\/doi.org\/10.1016\/S2589-7500(20)30060-1.","journal-title":"Lancet Digit Health"},{"issue":"1","key":"1596_CR28","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1186\/s12913-022-07655-6","volume":"22","author":"X-M Huang","year":"2022","unstructured":"Huang X-M, Yang B-F, Zheng W-L, Liu Q, Xiao F, Ouyang P-W, et al. Cost-effectiveness of artificial intelligence screening for diabetic retinopathy in rural China. BMC Health Serv Res. 2022;22(1):260. https:\/\/doi.org\/10.1186\/s12913-022-07655-6.","journal-title":"BMC Health Serv Res"},{"key":"1596_CR29","unstructured":"Taylor NP, FDA APPROVES FIRST AI DEVICE TO DETECT DIABETIC RETINOPATHY. https:\/\/www.fiercebiotech.com\/medtech\/fda-approves-diabetic-retinopathy-detecting-ai-algorithm (2018). Accessed."},{"issue":"1","key":"1596_CR30","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/s41746-024-01032-9","volume":"7","author":"Y Wang","year":"2024","unstructured":"Wang Y, Liu C, Hu W, Luo L, Shi D, Zhang J, et al. Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case. Npj Digit Med. 2024;7(1):43. https:\/\/doi.org\/10.1038\/s41746-024-01032-9.","journal-title":"Npj Digit Med"},{"key":"1596_CR31","doi-asserted-by":"publisher","first-page":"102387","DOI":"10.1016\/j.eclinm.2023.102387","volume":"67","author":"W Hu","year":"2024","unstructured":"Hu W, Joseph S, Li R, Woods E, Sun J, Shen M, et al. Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis. eClinicalMedicine. 2024;67:102387. https:\/\/doi.org\/10.1016\/j.eclinm.2023.102387.","journal-title":"eClinicalMedicine"},{"issue":"1","key":"1596_CR32","doi-asserted-by":"publisher","first-page":"4013","DOI":"10.1038\/s41598-024-54535-6","volume":"14","author":"H Riazi-Esfahani","year":"2024","unstructured":"Riazi-Esfahani H, Jafari B, Azimi H, Rahimi M, Saeidian J, Pouya P, et al. Assessment of area and structural irregularity of retinal layers in diabetic retinopathy using machine learning and image processing techniques. Sci Rep. 2024;14(1):4013. https:\/\/doi.org\/10.1038\/s41598-024-54535-6.","journal-title":"Sci Rep"},{"issue":"17","key":"1596_CR33","doi-asserted-by":"publisher","first-page":"52253","DOI":"10.1007\/s11042-023-17462-8","volume":"83","author":"P Saranya","year":"2023","unstructured":"Saranya P, Umamaheswari KM. Detection of exudates from retinal images for non-proliferative diabetic retinopathy detection using deep learning model. Multimedia Tools Appl. 2023;83(17):52253\u201373. https:\/\/doi.org\/10.1007\/s11042-023-17462-8.","journal-title":"Multimedia Tools Appl"},{"issue":"6","key":"1596_CR34","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1136\/bjo-2022-322683","volume":"108","author":"N Whitestone","year":"2024","unstructured":"Whitestone N, Nkurikiye J, Patnaik JL, Jaccard N, Lanouette G, Cherwek DH, et al. Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda. Br J Ophthalmol. 2024;108(6):840\u20135. https:\/\/doi.org\/10.1136\/bjo-2022-322683.","journal-title":"Br J Ophthalmol"},{"issue":"1","key":"1596_CR35","doi-asserted-by":"publisher","first-page":"e075055","DOI":"10.1136\/bmjopen-2023-075055","volume":"14","author":"CR Cleland","year":"2024","unstructured":"Cleland CR, Bascaran C, Makupa W, Shilio B, Sandi FA, Philippin H, et al. Artificial intelligence-supported diabetic retinopathy screening in Tanzania: rationale and design of a randomised controlled trial. BMJ Open. 2024;14(1):e075055\u2013e. https:\/\/doi.org\/10.1136\/bmjopen-2023-075055.","journal-title":"BMJ Open"},{"key":"1596_CR36","doi-asserted-by":"publisher","unstructured":"Kubin A-M, Huhtinen P, Ohtonen P, Keskitalo A, Wirkkala J, Hautala N. Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera. Ann Med. 2024;56(1). https:\/\/doi.org\/10.1080\/07853890.2024.2352018.","DOI":"10.1080\/07853890.2024.2352018"},{"key":"1596_CR37","doi-asserted-by":"publisher","unstructured":"Larsen TJ, Pettersen MB, Nygaard Jensen H, Lynge Pedersen M, Lund-Andersen H, J\u00f8rgensen ME, Byberg S. The use of artificial intelligence to assess diabetic eye disease among the Greenlandic population. Int J Circumpolar Health. 2024;83(1). https:\/\/doi.org\/10.1080\/22423982.2024.2314802.","DOI":"10.1080\/22423982.2024.2314802"},{"issue":"8","key":"1596_CR38","first-page":"1142","volume":"40","author":"Z Rui","year":"2024","unstructured":"Rui Z, Ying Z, Wenji NI, Ya H, Dandan LI, Tao JIN, Yong Z. Application value of artificial intelligence-based retinal microvascular analysis in diagnosis of diabetes complications. J Practical Med. 2024;40(8):1142.","journal-title":"J Practical Med"},{"issue":"8","key":"1596_CR39","doi-asserted-by":"publisher","first-page":"e0181359","DOI":"10.1371\/journal.pone.0181359","volume":"12","author":"PI Burgess","year":"2017","unstructured":"Burgess PI, Harding SP, Garc\u00eda-Fi\u00f1ana M, Beare NAV, Glover S, Cohen DB, et al. Incidence and progression of diabetic retinopathy in Sub-Saharan Africa: A five year cohort study. PLoS ONE. 2017;12(8):e0181359. https:\/\/doi.org\/10.1371\/journal.pone.0181359.","journal-title":"PLoS ONE"},{"issue":"5","key":"1596_CR40","doi-asserted-by":"publisher","first-page":"428","DOI":"10.4103\/0301-4738.100542","volume":"60","author":"Y Zheng","year":"2012","unstructured":"Zheng Y, He M, Congdon N. The worldwide epidemic of diabetic retinopathy. Indian J Ophthalmol. 2012;60(5):428\u201331. https:\/\/doi.org\/10.4103\/0301-4738.100542.","journal-title":"Indian J Ophthalmol"},{"key":"1596_CR41","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.diabres.2017.03.024","volume":"128","author":"K Ogurtsova","year":"2017","unstructured":"Ogurtsova K, da Rocha Fernandes JD, Huang Y, Linnenkamp U, Guariguata L, Cho NH, et al. IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017;128:40\u201350. https:\/\/doi.org\/10.1016\/j.diabres.2017.03.024.","journal-title":"Diabetes Res Clin Pract"},{"key":"1596_CR42","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.2147\/opth.S261629","volume":"14","author":"J Huemer","year":"2020","unstructured":"Huemer J, Wagner SK, Sim DA. The evolution of diabetic retinopathy screening programmes: A chronology of retinal photography from 35\u00a0mm slides to artificial intelligence. Clin Ophthalmol. 2020;14:2021\u201335. https:\/\/doi.org\/10.2147\/opth.S261629.","journal-title":"Clin Ophthalmol"},{"issue":"1","key":"1596_CR43","doi-asserted-by":"publisher","first-page":"e35","DOI":"10.1016\/s2589-7500(19)30004-4","volume":"1","author":"V Bellemo","year":"2019","unstructured":"Bellemo V, Lim ZW, Lim G, Nguyen QD, Xie Y, Yip MYT, et al. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. Lancet Digit Health. 2019;1(1):e35\u201344. https:\/\/doi.org\/10.1016\/s2589-7500(19)30004-4.","journal-title":"Lancet Digit Health"},{"issue":"4","key":"1596_CR44","doi-asserted-by":"publisher","first-page":"e235","DOI":"10.1016\/S2589-7500(22)00017-6","volume":"4","author":"P Ruamviboonsuk","year":"2022","unstructured":"Ruamviboonsuk P, Tiwari R, Sayres R, Nganthavee V, Hemarat K, Kongprayoon A, et al. Real-time diabetic retinopathy screening by deep learning in a multisite National screening programme: a prospective interventional cohort study. Lancet Digit Health. 2022;4(4):e235\u201344. https:\/\/doi.org\/10.1016\/S2589-7500(22)00017-6.","journal-title":"Lancet Digit Health"},{"issue":"6","key":"1596_CR45","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1038\/s41433-018-0064-9","volume":"32","author":"R Rajalakshmi","year":"2018","unstructured":"Rajalakshmi R, Subashini R, Anjana RM, Mohan V. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye. 2018;32(6):1138\u201344. https:\/\/doi.org\/10.1038\/s41433-018-0064-9.","journal-title":"Eye"},{"issue":"4","key":"1596_CR46","doi-asserted-by":"publisher","first-page":"356","DOI":"10.12809\/hkmj166078","volume":"23","author":"RL Wong","year":"2017","unstructured":"Wong RL, Tsang CW, Wong DS, McGhee S, Lam CH, Lian J, et al. Are we making good use of our public resources? The false-positive rate of screening by fundus photography for diabetic macular oedema. Hong Kong Med J. 2017;23(4):356\u201364. https:\/\/doi.org\/10.12809\/hkmj166078.","journal-title":"Hong Kong Med J"},{"issue":"5","key":"1596_CR47","doi-asserted-by":"publisher","first-page":"e14811","DOI":"10.1371\/journal.pone.0014811","volume":"6","author":"S Mackenzie","year":"2011","unstructured":"Mackenzie S, Schmermer C, Charnley A, Sim D, Vikas T, Dumskyj M, et al. SDOCT imaging to identify macular pathology in patients diagnosed with diabetic maculopathy by a digital photographic retinal screening programme. PLoS ONE. 2011;6(5):e14811. https:\/\/doi.org\/10.1371\/journal.pone.0014811.","journal-title":"PLoS ONE"},{"issue":"8","key":"1596_CR48","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1111\/aos.13936","volume":"96","author":"J Grauslund","year":"2018","unstructured":"Grauslund J, Andersen N, Andresen J, Flesner P, Haamann P, Heegaard S, et al. Evidence-based Danish guidelines for screening of diabetic retinopathy. Acta Ophthalmol. 2018;96(8):763\u20139. https:\/\/doi.org\/10.1111\/aos.13936.","journal-title":"Acta Ophthalmol"},{"issue":"8","key":"1596_CR49","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1016\/j.ophtha.2007.12.004","volume":"115","author":"DJ Browning","year":"2008","unstructured":"Browning DJ, Glassman AR, Aiello LP, Bressler NM, Bressler SB, Danis RP, et al. Optical coherence tomography measurements and analysis methods in optical coherence tomography studies of diabetic macular edema. Ophthalmology. 2008;115(8):1366\u201371. e1.","journal-title":"Ophthalmology"},{"issue":"4","key":"1596_CR50","doi-asserted-by":"publisher","first-page":"S67","DOI":"10.3928\/15428877-20110627-06","volume":"42","author":"PA Keane","year":"2011","unstructured":"Keane PA, Ruiz-Garcia H, Sadda SR. Clinical applications of long-wavelength (1,000-nm) optical coherence tomography. Ophthalmic Surg Lasers Imaging Retina. 2011;42(4):S67\u201374.","journal-title":"Ophthalmic Surg Lasers Imaging Retina"},{"issue":"1","key":"1596_CR51","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1038\/s41467-019-13922-8","volume":"11","author":"AV Varadarajan","year":"2020","unstructured":"Varadarajan AV, Bavishi P, Ruamviboonsuk P, Chotcomwongse P, Venugopalan S, Narayanaswamy A, et al. Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning. Nat Commun. 2020;11(1):130. https:\/\/doi.org\/10.1038\/s41467-019-13922-8.","journal-title":"Nat Commun"},{"issue":"5","key":"1596_CR52","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1097\/IAE.0000000000002992","volume":"41","author":"Q Wu","year":"2021","unstructured":"Wu Q, Zhang B, Hu Y, Liu B, Cao D, Yang D, DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES, et al. Retina. 2021;41(5):1110\u20137. https:\/\/doi.org\/10.1097\/IAE.0000000000002992.","journal-title":"Retina"},{"issue":"5","key":"1596_CR53","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.4103\/IJO.IJO_2614_22","volume":"71","author":"S Manikandan","year":"2023","unstructured":"Manikandan S, Raman R, Rajalakshmi R, Tamilselvi S, Surya RJ. Deep learning\u2013based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis. Indian J Ophthalmol. 2023;71(5):1783\u201396. https:\/\/doi.org\/10.4103\/IJO.IJO_2614_22.","journal-title":"Indian J Ophthalmol"},{"issue":"2","key":"1596_CR54","doi-asserted-by":"publisher","first-page":"304","DOI":"10.2337\/dc23-0993","volume":"47","author":"C Lam","year":"2024","unstructured":"Lam C, Wong YL, Tang Z, Hu X, Nguyen TX, Yang D, et al. Performance of artificial intelligence in detecting diabetic macular edema from fundus photography and optical coherence tomography images: A systematic review and Meta-analysis. Diabetes Care. 2024;47(2):304\u201319. https:\/\/doi.org\/10.2337\/dc23-0993.","journal-title":"Diabetes Care"},{"issue":"1","key":"1596_CR55","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1177\/11206721221094786","volume":"33","author":"H-Y Li","year":"2023","unstructured":"Li H-Y, Wang D-X, Dong L, Wei W-B. Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis. Eur J Ophthalmol. 2023;33(1):278\u201390. https:\/\/doi.org\/10.1177\/11206721221094786.","journal-title":"Eur J Ophthalmol"},{"key":"1596_CR56","doi-asserted-by":"crossref","unstructured":"Pavithra K, Kumar P, Geetha M, Bhandary S. Statistical Analysis of Deep Learning Models for Diabetic Macular Edema Classification using OCT Images. 2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER): IEEE; 2022. pp. 305\u2013\u200910.","DOI":"10.1109\/DISCOVER55800.2022.9974917"},{"issue":"1","key":"1596_CR57","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s12938-022-01018-2","volume":"21","author":"R Liu","year":"2022","unstructured":"Liu R, Li Q, Xu F, Wang S, He J, Cao Y, et al. Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital. Biomed Eng Online. 2022;21(1):47. https:\/\/doi.org\/10.1186\/s12938-022-01018-2.","journal-title":"Biomed Eng Online"},{"issue":"1","key":"1596_CR58","doi-asserted-by":"publisher","first-page":"23024","DOI":"10.1038\/s41598-021-02479-6","volume":"11","author":"G Ryu","year":"2021","unstructured":"Ryu G, Lee K, Park D, Park SH, Sagong M. A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography. Sci Rep. 2021;11(1):23024. https:\/\/doi.org\/10.1038\/s41598-021-02479-6.","journal-title":"Sci Rep"},{"issue":"10","key":"1596_CR59","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1001\/jama.1993.03500100088034","volume":"269","author":"FL Ferris","year":"1993","unstructured":"Ferris FL. How effective are treatments for diabetic retinopathy? JAMA. 1993;269(10):1290\u20131.","journal-title":"JAMA"},{"issue":"7013","key":"1596_CR60","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1136\/bmj.311.7013.1131","volume":"311","author":"SP Harding","year":"1995","unstructured":"Harding SP, Broadbent DM, Neoh C, White MC, Vora J. Sensitivity and specificity of photography and direct ophthalmoscopy in screening for sight threatening eye disease: the Liverpool diabetic eye study. BMJ. 1995;311(7013):1131\u20135. https:\/\/doi.org\/10.1136\/bmj.311.7013.1131.","journal-title":"BMJ"},{"issue":"13","key":"1596_CR61","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1167\/tvst.10.13.10","volume":"10","author":"X Ye","year":"2021","unstructured":"Ye X, Wang J, Chen Y, Lv Z, He S, Mao J, et al. Automatic screening and identifying myopic maculopathy on optical coherence tomography images using deep learning. Translational Vis Sci Technol. 2021;10(13):10.","journal-title":"Translational Vis Sci Technol"},{"issue":"22","key":"1596_CR62","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. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402\u201310.","journal-title":"JAMA"},{"issue":"6","key":"1596_CR63","doi-asserted-by":"publisher","first-page":"e0179790","DOI":"10.1371\/journal.pone.0179790","volume":"12","author":"H Takahashi","year":"2017","unstructured":"Takahashi H, Tampo H, Arai Y, Inoue Y, Kawashima H. Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy. PLoS ONE. 2017;12(6):e0179790.","journal-title":"PLoS ONE"},{"issue":"1","key":"1596_CR64","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s41746-019-0099-8","volume":"2","author":"P Ruamviboonsuk","year":"2019","unstructured":"Ruamviboonsuk P, Krause J, Chotcomwongse P, Sayres R, Raman R, Widner K, et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med. 2019;2(1):25.","journal-title":"NPJ Digit Med"},{"key":"1596_CR65","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1007\/s11517-017-1638-6","volume":"55","author":"Q Abbas","year":"2017","unstructured":"Abbas Q, Fondon I, Sarmiento A, Jim\u00e9nez S, Alemany P. Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features. Med Biol Eng Comput. 2017;55:1959\u201374.","journal-title":"Med Biol Eng Comput"},{"issue":"22","key":"1596_CR66","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","volume":"318","author":"DSW Ting","year":"2017","unstructured":"Ting DSW, Cheung CY-L, Lim G, Tan GSW, Quang ND, Gan A, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017;318(22):2211\u201323.","journal-title":"JAMA"},{"issue":"12","key":"1596_CR67","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, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care. 2018;41(12):2509\u201316.","journal-title":"Diabetes Care"},{"issue":"4","key":"1596_CR68","doi-asserted-by":"publisher","first-page":"e3445","DOI":"10.1002\/dmrr.3445","volume":"37","author":"Y Wang","year":"2021","unstructured":"Wang Y, Yu M, Hu B, Jin X, Li Y, Zhang X, et al. Deep learning-based detection and stage grading for optimising diagnosis of diabetic retinopathy. Diab\/Metab Res Rev. 2021;37(4):e3445.","journal-title":"Diab\/Metab Res Rev"},{"issue":"4","key":"1596_CR69","doi-asserted-by":"publisher","first-page":"4612","DOI":"10.22214\/ijraset.2023.51304","volume":"11","author":"BP Aishwarya","year":"2023","unstructured":"Aishwarya BP, Chandan MS, Preethu KS, Swathi KA. Diabetic retinopathy using inception V3 model. Int J Res Appl Sci Eng Technol. 2023;11(4):4612\u20136. https:\/\/doi.org\/10.22214\/ijraset.2023.51304","journal-title":"Int J Res Appl Sci Eng Technol"},{"key":"1596_CR70","doi-asserted-by":"crossref","unstructured":"Prajapati M, Baliarsingh SK, Hota J. MobileNetV3 based Classification Model for Diabetic Retinopathy. 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN): IEEE; 2023. pp. 213-7.","DOI":"10.1109\/SPIN57001.2023.10117341"},{"key":"1596_CR71","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.ajo.2020.01.016","volume":"216","author":"HS Sandhu","year":"2020","unstructured":"Sandhu HS, Elmogy M, Sharafeldeen AT, Elsharkawy M, El-Adawy N, Eltanboly A, et al. Automated diagnosis of diabetic retinopathy using clinical biomarkers, optical coherence tomography, and optical coherence tomography angiography. Am J Ophthalmol. 2020;216:201\u20136.","journal-title":"Am J Ophthalmol"},{"key":"1596_CR72","doi-asserted-by":"publisher","first-page":"2611250","DOI":"10.1155\/2021\/2611250","volume":"2021","author":"X Wang","year":"2021","unstructured":"Wang X, Ji Z, Ma X, Zhang Z, Yi Z, Zheng H, et al. Automated grading of diabetic retinopathy with Ultra-Widefield fluorescein angiography and deep learning. J Diabetes Res. 2021;2021:2611250. https:\/\/doi.org\/10.1155\/2021\/2611250.","journal-title":"J Diabetes Res"},{"issue":"3","key":"1596_CR73","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1111\/dom.13552","volume":"21","author":"M Garc\u00eda-Fi\u00f1ana","year":"2019","unstructured":"Garc\u00eda-Fi\u00f1ana M, Hughes DM, Cheyne CP, Broadbent DM, Wang A, Kom\u00e1rek A, et al. Personalized risk-based screening for diabetic retinopathy: A multivariate approach versus the use of stratification rules. Diabetes Obes Metab. 2019;21(3):560\u20138. https:\/\/doi.org\/10.1111\/dom.13552.","journal-title":"Diabetes Obes Metab"},{"issue":"10","key":"1596_CR74","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1007\/s00125-011-2257-7","volume":"54","author":"T Aspelund","year":"2011","unstructured":"Aspelund T, \u00de\u00f3risd\u00f3ttir \u00d3, \u00d3lafsdottir E, Gudmundsdottir A, Einarsd\u00f3ttir AB, Mehlsen J, et al. Individual risk assessment and information technology to optimise screening frequency for diabetic retinopathy. Diabetologia. 2011;54(10):2525. https:\/\/doi.org\/10.1007\/s00125-011-2257-7.","journal-title":"Diabetologia"},{"issue":"4","key":"1596_CR75","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.jdiacomp.2014.12.020","volume":"29","author":"E Soto-Pedre","year":"2015","unstructured":"Soto-Pedre E, Pinies JA, Hernaez-Ortega MC. External validation of a risk assessment model to adjust the frequency of eye-screening visits in patients with diabetes mellitus. J Diabetes Complications. 2015;29(4):508\u201311. https:\/\/doi.org\/10.1016\/j.jdiacomp.2014.12.020.","journal-title":"J Diabetes Complications"},{"key":"1596_CR76","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.dss.2017.05.012","volume":"101","author":"S Piri","year":"2017","unstructured":"Piri S, Delen D, Liu T, Zolbanin HM. A data analytics approach to Building a clinical decision support system for diabetic retinopathy: developing and deploying a model ensemble. Decis Support Syst. 2017;101:12\u201327. https:\/\/doi.org\/10.1016\/j.dss.2017.05.012.","journal-title":"Decis Support Syst"},{"issue":"5","key":"1596_CR77","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/S0161-6420(13)38012-9","volume":"98","author":"ETDRSR Group","year":"1991","unstructured":"Group ETDRSR. Grading diabetic retinopathy from stereoscopic color fundus photographs\u2014an extension of the modified airlie house classification: ETDRS report number 10. Ophthalmology. 1991;98(5):786\u2013806.","journal-title":"Ophthalmology"},{"key":"1596_CR78","doi-asserted-by":"publisher","first-page":"6713","DOI":"10.1109\/IEMBS.2010.5626245","volume":"IEEE","author":"M Skevofilakas","year":"2010","unstructured":"Skevofilakas M, Zarkogianni K, Karamanos BG, Nikita KS. A hybrid decision support system for the risk assessment of retinopathy development as a long term complication of type 1 diabetes mellitus. 2010 Annual Int Conf IEEE Eng Med Biology. 2010;IEEE:6713\u20136.","journal-title":"2010 Annual Int Conf IEEE Eng Med Biology"},{"issue":"1","key":"1596_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-019-0172-3","volume":"2","author":"F Arcadu","year":"2019","unstructured":"Arcadu F, Benmansour F, Maunz A, Willis J, Haskova Z, Prunotto M. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit Med. 2019;2(1):1\u20139.","journal-title":"NPJ Digit Med"},{"issue":"3","key":"1596_CR80","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1001\/jamaophthalmol.2023.6318","volume":"142","author":"PS Silva","year":"2024","unstructured":"Silva PS, Zhang D, Jacoba CMP, Fickweiler W, Lewis D, Leitmeyer J, et al. Automated machine learning for predicting diabetic retinopathy progression from Ultra-Widefield retinal images. JAMA Ophthalmol. 2024;142(3):171. https:\/\/doi.org\/10.1001\/jamaophthalmol.2023.6318.","journal-title":"JAMA Ophthalmol"},{"issue":"1","key":"1596_CR81","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/s41467-023-44676-z","volume":"15","author":"RM Wolf","year":"2024","unstructured":"Wolf RM, Channa R, Liu TYA, Zehra A, Bromberger L, Patel D, et al. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial. Nat Commun. 2024;15(1):421. https:\/\/doi.org\/10.1038\/s41467-023-44676-z.","journal-title":"Nat Commun"},{"issue":"4","key":"1596_CR82","doi-asserted-by":"publisher","first-page":"336","DOI":"10.3928\/1542-8877-20050701-15","volume":"36","author":"PJ Rosenfeld","year":"2005","unstructured":"Rosenfeld PJ, Fung AE, Puliafito CA. Optical coherence tomography findings after an intravitreal injection of bevacizumab (avastin) for macular edema from central retinal vein occlusion. Ophthalmic Surg Lasers Imaging. 2005;36(4):336\u20139.","journal-title":"Ophthalmic Surg Lasers Imaging"},{"issue":"2","key":"1596_CR83","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1038\/nrd1955","volume":"5","author":"EW Ng","year":"2006","unstructured":"Ng EW, Shima DT, Calias P, Cunningham ET Jr., Guyer DR, Adamis AP. Pegaptanib, a targeted anti-VEGF aptamer for ocular vascular disease. Nat Rev Drug Discov. 2006;5(2):123\u201332. https:\/\/doi.org\/10.1038\/nrd1955.","journal-title":"Nat Rev Drug Discov"},{"issue":"10","key":"1596_CR84","doi-asserted-by":"publisher","first-page":"1706","DOI":"10.1016\/j.ophtha.2006.04.033","volume":"113","author":"DW Chun","year":"2006","unstructured":"Chun DW, Heier JS, Topping TM, Duker JS, Bankert JM. A pilot study of multiple intravitreal injections of Ranibizumab in patients with center-involving clinically significant diabetic macular edema. Ophthalmology. 2006;113(10):1706\u201312. https:\/\/doi.org\/10.1016\/j.ophtha.2006.04.033.","journal-title":"Ophthalmology"},{"issue":"6","key":"1596_CR85","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1016\/j.ajo.2006.06.068","volume":"142","author":"QD Nguyen","year":"2006","unstructured":"Nguyen QD, Tatlipinar S, Shah SM, Haller JA, Quinlan E, Sung J, et al. Vascular endothelial growth factor is a critical stimulus for diabetic macular edema. Am J Ophthalmol. 2006;142(6):961\u20139. https:\/\/doi.org\/10.1016\/j.ajo.2006.06.068.","journal-title":"Am J Ophthalmol"},{"issue":"2","key":"1596_CR86","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1364\/BOE.379150","volume":"11","author":"R Rasti","year":"2020","unstructured":"Rasti R, Allingham MJ, Mettu PS, Kavusi S, Govind K, Cousins SW, Farsiu S. Deep learning-based single-shot prediction of differential effects of anti-VEGF treatment in patients with diabetic macular edema. Biomedical Opt Express. 2020;11(2):1139. https:\/\/doi.org\/10.1364\/BOE.379150.","journal-title":"Biomedical Opt Express"},{"issue":"5","key":"1596_CR87","doi-asserted-by":"publisher","first-page":"2441","DOI":"10.1007\/s40123-023-00746-5","volume":"12","author":"X Ye","year":"2023","unstructured":"Ye X, Gao K, He S, Zhong X, Shen Y, Wang Y, et al. Artificial Intelligence-Based quantification of central macular fluid volume and VA prediction for diabetic macular edema using OCT images. Ophthalmol Therapy. 2023;12(5):2441\u201352. https:\/\/doi.org\/10.1007\/s40123-023-00746-5.","journal-title":"Ophthalmol Therapy"},{"key":"1596_CR88","doi-asserted-by":"crossref","unstructured":"Liu B, Zhang B, Hu Y, Cao D, Yang D, Wu Q et al. Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning. Annals Translational Med. 2021;9(1).","DOI":"10.21037\/atm-20-1431"},{"issue":"7","key":"1596_CR89","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.oret.2021.05.002","volume":"5","author":"M Gallardo","year":"2021","unstructured":"Gallardo M, Munk MR, Kurmann T, De Zanet S, Mosinska A, Karagoz IK, et al. Machine learning can predict Anti-VEGF treatment demand in a Treat-and-Extend regimen for patients with neovascular AMD, DME, and RVO associated macular edema. Ophthalmol Retina. 2021;5(7):604\u201324. https:\/\/doi.org\/10.1016\/j.oret.2021.05.002.","journal-title":"Ophthalmol Retina"},{"key":"1596_CR90","unstructured":"Zamytskiy \u0415\u0410ZA, Karlova E, Zamytskiy P. Analysis of the coagulates intensity in laser treatment of diabetic macular edema in a Navilas robotic laser system. 2017."},{"issue":"2","key":"1596_CR91","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.sjopt.2014.09.001","volume":"29","author":"I Kozak","year":"2015","unstructured":"Kozak I, Luttrull JK. Modern retinal laser therapy. Saudi J Ophthalmol. 2015;29(2):137\u201346. https:\/\/doi.org\/10.1016\/j.sjopt.2014.09.001.","journal-title":"Saudi J Ophthalmol"},{"key":"1596_CR92","doi-asserted-by":"crossref","unstructured":"Moutray T, Evans JR, Lois N, Armstrong DJ, Peto T, Azuara-Blanco A. Different lasers and techniques for proliferative diabetic retinopathy. Cochrane Database Syst Reviews. 2018(3).","DOI":"10.1002\/14651858.CD012314.pub2"},{"key":"1596_CR93","doi-asserted-by":"crossref","unstructured":"Ilyasova N, Demin N, Shirokanev A, Andriyanov N. Automated system for the personalization of retinal laser treatment in diabetic retinopathy based on the intelligent analysis of OCT data and fundus images. Intelligent decision technologies. Springer; 2022. pp. 171\u201381.","DOI":"10.1007\/978-981-19-3444-5_15"},{"key":"1596_CR94","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.ijcard.2018.03.099","volume":"262","author":"SC Larsson","year":"2018","unstructured":"Larsson SC, Wallin A, H\u00e5kansson N, Stackelberg O, B\u00e4ck M, Wolk A. Type 1 and type 2 diabetes mellitus and incidence of seven cardiovascular diseases. Int J Cardiol. 2018;262:66\u201370. https:\/\/doi.org\/10.1016\/j.ijcard.2018.03.099.","journal-title":"Int J Cardiol"},{"key":"1596_CR95","doi-asserted-by":"publisher","first-page":"100072","DOI":"10.1016\/j.ibmed.2022.100072","volume":"6","author":"M Wang","year":"2022","unstructured":"Wang M, Francis F, Kunz H, Zhang X, Wan C, Liu Y, et al. Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: A systematic review. Intelligence-Based Med. 2022;6:100072. https:\/\/doi.org\/10.1016\/j.ibmed.2022.100072.","journal-title":"Intelligence-Based Med"},{"key":"1596_CR96","doi-asserted-by":"publisher","first-page":"19053","DOI":"10.1038\/srep19053","volume":"6","author":"VY Guo","year":"2016","unstructured":"Guo VY, Chan JC, Chung H, Ozaki R, So W, Luk A, et al. Retinal information is independently associated with cardiovascular disease in patients with type 2 diabetes. Sci Rep. 2016;6:19053. https:\/\/doi.org\/10.1038\/srep19053.","journal-title":"Sci Rep"},{"issue":"3","key":"1596_CR97","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","volume":"2","author":"R Poplin","year":"2018","unstructured":"Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomedical Eng. 2018;2(3):158\u201364. https:\/\/doi.org\/10.1038\/s41551-018-0195-0.","journal-title":"Nat Biomedical Eng"},{"issue":"3","key":"1596_CR98","doi-asserted-by":"publisher","first-page":"710","DOI":"10.2337\/dc21-1124","volume":"45","author":"IR Mordi","year":"2022","unstructured":"Mordi IR, Trucco E, Syed MG, MacGillivray T, Nar A, Huang Y, et al. Prediction of major adverse cardiovascular events from retinal, clinical, and genomic data in individuals with type 2 diabetes: A population cohort study. Diabetes Care. 2022;45(3):710\u20136. https:\/\/doi.org\/10.2337\/dc21-1124.","journal-title":"Diabetes Care"},{"key":"1596_CR99","doi-asserted-by":"publisher","unstructured":"Juster-Switlyk K, Smith AG. Updates in diabetic peripheral neuropathy. F1000Res. 2016;5. https:\/\/doi.org\/10.12688\/f1000research.7898.1.","DOI":"10.12688\/f1000research.7898.1"},{"issue":"6","key":"1596_CR100","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.2337\/diacare.26.6.1790","volume":"26","author":"A Gordois","year":"2003","unstructured":"Gordois A, Scuffham P, Shearer A, Oglesby A, Tobian JA. The health care costs of diabetic peripheral neuropathy in the US. Diabetes Care. 2003;26(6):1790\u20135. https:\/\/doi.org\/10.2337\/diacare.26.6.1790.","journal-title":"Diabetes Care"},{"issue":"Suppl 1","key":"1596_CR101","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1002\/dmrr.360","volume":"19","author":"C Quattrini","year":"2003","unstructured":"Quattrini C, Tesfaye S. Understanding the impact of painful diabetic neuropathy. Diabetes Metab Res Rev. 2003;19(Suppl 1):S2\u20138. https:\/\/doi.org\/10.1002\/dmrr.360.","journal-title":"Diabetes Metab Res Rev"},{"issue":"1","key":"1596_CR102","doi-asserted-by":"publisher","first-page":"136","DOI":"10.2337\/dc16-2042","volume":"40","author":"R Pop-Busui","year":"2017","unstructured":"Pop-Busui R, Boulton AJ, Feldman EL, Bril V, Freeman R, Malik RA, et al. Diabetic neuropathy: a position statement by the American diabetes association. Diabetes Care. 2017;40(1):136.","journal-title":"Diabetes Care"},{"issue":"4","key":"1596_CR103","doi-asserted-by":"publisher","first-page":"255","DOI":"10.4093\/dmj.2018.0056","volume":"42","author":"IN Petropoulos","year":"2018","unstructured":"Petropoulos IN, Ponirakis G, Khan A, Almuhannadi H, Gad H, Malik RA. Diagnosing diabetic neuropathy: something old, something new. Diabetes Metab J. 2018;42(4):255\u201369. https:\/\/doi.org\/10.4093\/dmj.2018.0056.","journal-title":"Diabetes Metab J"},{"key":"1596_CR104","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1109\/embc44109.2020.9175982","volume":"2020","author":"J Benson","year":"2020","unstructured":"Benson J, Estrada T, Burge M, Soliz P. Diabetic peripheral neuropathy risk assessment using digital fundus photographs and machine learning. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:1988\u201391. https:\/\/doi.org\/10.1109\/embc44109.2020.9175982.","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"issue":"11","key":"1596_CR105","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.3390\/diagnostics11111943","volume":"11","author":"DR Cervera","year":"2021","unstructured":"Cervera DR, Smith L, Diaz-Santana L, Kumar M, Raman R, Sivaprasad S. Identifying peripheral neuropathy in colour fundus photographs based on deep learning. Diagnostics. 2021;11(11):1943. https:\/\/doi.org\/10.3390\/diagnostics11111943.","journal-title":"Diagnostics"},{"issue":"10","key":"1596_CR106","doi-asserted-by":"publisher","first-page":"6547","DOI":"10.1167\/iovs.14-15001","volume":"55","author":"WJ Lee","year":"2014","unstructured":"Lee WJ, Sobrin L, Lee MJ, Kang MH, Seong M, Cho H. The relationship between diabetic retinopathy and diabetic nephropathy in a Population-Based study in Korea (KNHANES V-2, 3). Investig Ophthalmol Vis Sci. 2014;55(10):6547\u201353. https:\/\/doi.org\/10.1167\/iovs.14-15001.","journal-title":"Investig Ophthalmol Vis Sci"},{"key":"1596_CR107","doi-asserted-by":"publisher","first-page":"S333","DOI":"10.1016\/j.dsx.2017.03.011","volume":"11","author":"MH Ahmed","year":"2017","unstructured":"Ahmed MH, Elwali ES, Awadalla H, Almobarak AO. The relationship between diabetic retinopathy and nephropathy in Sudanese adult with diabetes: population based study. Diabetes Metabolic Syndrome: Clin Res Reviews. 2017;11:S333\u20136. https:\/\/doi.org\/10.1016\/j.dsx.2017.03.011.","journal-title":"Diabetes Metabolic Syndrome: Clin Res Reviews"},{"issue":"4","key":"1596_CR108","doi-asserted-by":"publisher","first-page":"251","DOI":"10.3109\/09286586.2010.498661","volume":"17","author":"R-A Pedro","year":"2010","unstructured":"Pedro R-A, Ramon S-A, Marc B-B, Juan F-B, Isabel M-M. Prevalence and relationship between diabetic retinopathy and nephropathy, and its risk factors in the North-East of Spain, a Population-Based study. Ophthalmic Epidemiol. 2010;17(4):251\u201365. https:\/\/doi.org\/10.3109\/09286586.2010.498661.","journal-title":"Ophthalmic Epidemiol"},{"key":"1596_CR109","doi-asserted-by":"publisher","first-page":"3922398","DOI":"10.1155\/2022\/3922398","volume":"2022","author":"M Gupta","year":"2022","unstructured":"Gupta M, Rao IR, Nagaraju SP, Bhandary SV, Gupta J, Babu GTC. Diabetic retinopathy is a predictor of progression of diabetic kidney disease: A systematic review and Meta-Analysis. Int J Nephrol. 2022;2022:3922398. https:\/\/doi.org\/10.1155\/2022\/3922398.","journal-title":"Int J Nephrol"},{"key":"1596_CR110","first-page":"151336","volume-title":"Chronic kidney disease burden in low-resource settings: regional perspectives","author":"II Ulasi","year":"2022","unstructured":"Ulasi II, Awobusuyi O, Nayak S, Ramachandran R, Musso CG, Depine SA, et al. Chronic kidney disease burden in low-resource settings: regional perspectives. Seminars in Nephrology: Elsevier; 2022. p. 151336."},{"issue":"10225","key":"1596_CR111","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/S0140-6736(20)30045-3","volume":"395","author":"B Bikbov","year":"2020","unstructured":"Bikbov B, Purcell CA, Levey AS, Smith M, Abdoli A, Abebe M, et al. Global, regional, and National burden of chronic kidney disease, 1990\u20132017: a systematic analysis for the global burden of disease study 2017. Lancet. 2020;395(10225):709\u201333.","journal-title":"Lancet"},{"issue":"10159","key":"1596_CR112","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.1016\/S0140-6736(18)31694-5","volume":"392","author":"KJ Foreman","year":"2018","unstructured":"Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016\u201340 for 195 countries and territories. Lancet. 2018;392(10159):2052\u201390.","journal-title":"Lancet"},{"issue":"1","key":"1596_CR113","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/kisup.2012.77","volume":"3","author":"G Eknoyan","year":"2013","unstructured":"Eknoyan G, Lameire N, Eckardt K, Kasiske B, Wheeler D, Levin A, et al. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 2013;3(1):5\u201314.","journal-title":"Kidney Int"},{"issue":"4","key":"1596_CR114","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.2337\/diabetes.52.4.1036","volume":"52","author":"ML Caramori","year":"2003","unstructured":"Caramori ML, Fioretto P, Mauer M. Low glomerular filtration rate in normoalbuminuric type 1 diabetic patients: an indicator of more advanced glomerular lesions. Diabetes. 2003;52(4):1036\u201340.","journal-title":"Diabetes"},{"issue":"6","key":"1596_CR115","doi-asserted-by":"publisher","first-page":"e295","DOI":"10.1016\/s2589-7500(20)30063-7","volume":"2","author":"C Sabanayagam","year":"2020","unstructured":"Sabanayagam C, Xu D, Ting DSW, Nusinovici S, Banu R, Hamzah H, et al. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Health. 2020;2(6):e295\u2013302. https:\/\/doi.org\/10.1016\/s2589-7500(20)30063-7.","journal-title":"Lancet Digit Health"},{"issue":"6","key":"1596_CR116","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/s41551-021-00745-6","volume":"5","author":"K Zhang","year":"2021","unstructured":"Zhang K, Liu X, Xu J, Yuan J, Cai W, Chen T, et al. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images. Nat Biomedical Eng. 2021;5(6):533\u201345. https:\/\/doi.org\/10.1038\/s41551-021-00745-6.","journal-title":"Nat Biomedical Eng"},{"key":"1596_CR117","doi-asserted-by":"publisher","unstructured":"Zhao L, Ren H, Zhang J, Cao Y, Wang Y, Meng D, CLASSIFIED USING THE LESION-AWARE DEEP LEARNING SYSTEM, PREDICTS DIABETIC END-STAGE RENAL DISEASE IN CHINESE PATIENTS. DIABETIC RETINOPATHY,. Endocr Pract. 2020;26(4):429\u2013\u200943. https:\/\/doi.org\/10.4158\/ep-2019-0512","DOI":"10.4158\/ep-2019-0512"},{"issue":"S1","key":"1596_CR118","doi-asserted-by":"publisher","first-page":"i63","DOI":"10.1007\/s00392-006-1123-y","volume":"95","author":"C Diehm","year":"2006","unstructured":"Diehm C, Lawall H, Diabetes. Herzchirurgie und periphere gef\u00e4\u00dfe. Clin Res Cardiol. 2006;95(S1):i63\u20139. https:\/\/doi.org\/10.1007\/s00392-006-1123-y.","journal-title":"Clin Res Cardiol"},{"issue":"5","key":"1596_CR119","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.2337\/dc13-2303","volume":"37","author":"AD Althouse","year":"2014","unstructured":"Althouse AD, Abbott JD, Forker AD, Bertolet M, Barinas-Mitchell E, Thurston RC, et al. Risk factors for incident peripheral arterial disease in type 2 diabetes: results from the bypass angioplasty revascularization investigation in type 2 diabetes (BARI 2D) trial. Diabetes Care. 2014;37(5):1346\u201352. https:\/\/doi.org\/10.2337\/dc13-2303.","journal-title":"Diabetes Care"},{"issue":"1","key":"1596_CR120","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1111\/j.1742-1241.2006.01168.x","volume":"61","author":"SI Yamagishi","year":"2007","unstructured":"Yamagishi SI, Nakamura K, Matsui T, Ueda SI, Imaizumi T. Role of postprandial hyperglycaemia in cardiovascular disease in diabetes. Int J Clin Pract. 2007;61(1):83\u20137. https:\/\/doi.org\/10.1111\/j.1742-1241.2006.01168.x.","journal-title":"Int J Clin Pract"},{"issue":"16","key":"1596_CR121","doi-asserted-by":"publisher","first-page":"9801","DOI":"10.3390\/ijerph19169801","volume":"19","author":"A Achim","year":"2022","unstructured":"Achim A, Stanek A, Homorodean C, Spinu M, Onea HL, Laz\u0103r L, et al. Approaches to peripheral artery disease in diabetes: are there any differences?? Int J Environ Res Public Health. 2022;19(16):9801. https:\/\/doi.org\/10.3390\/ijerph19169801.","journal-title":"Int J Environ Res Public Health"},{"key":"1596_CR122","doi-asserted-by":"crossref","unstructured":"Hirsch AT, Kalsi H, Rooke TW. Peripheral arterial diseases. Cardiovascular Medicine. Springer; 2007. pp. 1681\u2013703.","DOI":"10.1007\/978-1-84628-715-2_81"},{"issue":"1","key":"1596_CR123","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1038\/s41598-022-05169-z","volume":"12","author":"S Mueller","year":"2022","unstructured":"Mueller S, Wintergerst MWM, Falahat P, Holz FG, Schaefer C, Schahab N, et al. Multiple instance learning detects peripheral arterial disease from high-resolution color fundus photography. Sci Rep. 2022;12(1):1389. https:\/\/doi.org\/10.1038\/s41598-022-05169-z.","journal-title":"Sci Rep"},{"issue":"1","key":"1596_CR124","doi-asserted-by":"publisher","first-page":"5","DOI":"10.35711\/aimi.v2.i1.5","volume":"2","author":"BB Panda","year":"2021","unstructured":"Panda BB, Thakur S, Mohapatra S, Parida S. Artificial intelligence in ophthalmology: A new era is beginning. Artif Intell Med Imaging. 2021;2(1):5\u201312. https:\/\/doi.org\/10.35711\/aimi.v2.i1.5.","journal-title":"Artif Intell Med Imaging"},{"key":"1596_CR125","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition2016. pp. 770-8.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1596_CR126","doi-asserted-by":"crossref","unstructured":"Kaiming H, Xiangyu Z, Shaoqing R, Jian S. Identity mappings in deep residual networks. European conference on computer vision: Springer; 2016. pp. 630\u2013\u200945.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"1596_CR127","unstructured":"Kurama V. A Review of Popular Deep Learning Architectures: ResNet, InceptionV3, and SqueezeNet. https:\/\/blog.paperspace.com\/popular-deep-learning-architectures-resnet-inceptionv3-squeezenet\/ (2020). Accessed."},{"key":"1596_CR128","unstructured":"Anwar A. Difference between Alexnet, Vggnet, Resnet and inception. Medium-Towards Data Sci. 2019."},{"key":"1596_CR129","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014. https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"1596_CR130","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2402.09975","author":"A Jafarizadeh","year":"2024","unstructured":"Jafarizadeh A, Maleki SF, Pouya P, Sobhi N, Abdollahi M, Pedrammehr S, et al. Current and future roles of artificial intelligence in retinopathy of prematurity. arXiv preprint arXiv:2402.09975. 2024. https:\/\/doi.org\/10.48550\/arXiv.2402.09975","journal-title":"ArXiv Preprint arXiv:240209975"},{"key":"1596_CR131","unstructured":"Rosebrock A. Imagenet: Vggnet, Resnet, inception, and Xception with Keras. Mars. 2017."},{"key":"1596_CR132","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS. Deep learning for visual understanding: A review. Neurocomputing. 2016;187:27\u201348.","journal-title":"Neurocomputing"},{"key":"1596_CR133","doi-asserted-by":"publisher","unstructured":"Farabi Maleki S, Yousefi M, Afshar S, Pedrammehr S, Lim CP, Jafarizadeh A, Asadi H. Artificial intelligence for multiple sclerosis management using retinal images: Pearl, peaks, and pitfalls. Semin Ophthalmol. 2023;1\u201318. https:\/\/doi.org\/10.1080\/08820538.2023.2293030.","DOI":"10.1080\/08820538.2023.2293030"},{"key":"1596_CR134","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition2016. pp. 2818-26.","DOI":"10.1109\/CVPR.2016.308"},{"key":"1596_CR135","unstructured":"Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. International conference on machine learning: pmlr; 2015. pp. 448\u2013\u200956."},{"key":"1596_CR136","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition2017. pp. 4700-8.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1596_CR137","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25."},{"key":"1596_CR138","doi-asserted-by":"crossref","unstructured":"Zhang H, Wu C, Zhang Z, Zhu Y, Lin H, Zhang Z et al. Resnest: Split-attention networks. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition2022. pp. 2736-46.","DOI":"10.1109\/CVPRW56347.2022.00309"},{"key":"1596_CR139","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005.","journal-title":"Med Image Anal"},{"key":"1596_CR140","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18: Springer; 2015. pp. 234\u2013\u200941.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1596_CR141","doi-asserted-by":"publisher","unstructured":"Jeong Y, Hong YJ, Han JH. Review of machine learning applications using retinal fundus images. Diagnostics (Basel). 2022;12(1). https:\/\/doi.org\/10.3390\/diagnostics12010134.","DOI":"10.3390\/diagnostics12010134"},{"issue":"10","key":"1596_CR142","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2010;22(10):1345\u201359. https:\/\/doi.org\/10.1109\/TKDE.2009.191.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1596_CR143","doi-asserted-by":"crossref","unstructured":"Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. A survey on deep transfer learning. Artificial Neural Networks and Machine Learning\u2013ICANN. 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4\u20137, 2018, Proceedings, Part III 27: Springer; 2018. pp. 270-9.","DOI":"10.1007\/978-3-030-01424-7_27"},{"issue":"6","key":"1596_CR144","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.18240\/ijo.2024.06.03","volume":"17","author":"X-Y Deng","year":"2024","unstructured":"Deng X-Y, Zhang Z-X, Li H-X, Wang J, Chen Y-Q, Mao J-B, et al. Retinal vascular morphological characteristics in diabetic retinopathy: an artificial intelligence study using a transfer learning system to analyze ultra-wide field images. Int J Ophthalmol. 2024;17(6):1001\u20136. https:\/\/doi.org\/10.18240\/ijo.2024.06.03.","journal-title":"Int J Ophthalmol"},{"issue":"5","key":"1596_CR145","doi-asserted-by":"publisher","first-page":"e182665","DOI":"10.1001\/jamanetworkopen.2018.2665","volume":"1","author":"Y Kanagasingam","year":"2018","unstructured":"Kanagasingam Y, Xiao D, Vignarajan J, Preetham A, Tay-Kearney M-L, Mehrotra A. Evaluation of artificial Intelligence\u2013Based grading of diabetic retinopathy in primary care. JAMA Netw Open. 2018;1(5):e182665\u2013e. https:\/\/doi.org\/10.1001\/jamanetworkopen.2018.2665.","journal-title":"JAMA Netw Open"},{"issue":"10","key":"1596_CR146","doi-asserted-by":"publisher","first-page":"2685","DOI":"10.1038\/s41433-021-01572-4","volume":"35","author":"T Das","year":"2021","unstructured":"Das T, Takkar B, Sivaprasad S, Thanksphon T, Taylor H, Wiedemann P, et al. Recently updated global diabetic retinopathy screening guidelines: commonalities, differences, and future possibilities. Eye. 2021;35(10):2685\u201398. https:\/\/doi.org\/10.1038\/s41433-021-01572-4.","journal-title":"Eye"},{"issue":"10","key":"1596_CR147","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1136\/bjophthalmol-2016-310002","volume":"101","author":"LZ Wang","year":"2017","unstructured":"Wang LZ, Cheung CY, Tapp RJ, Hamzah H, Tan G, Ting D, et al. Availability and variability in guidelines on diabetic retinopathy screening in Asian countries. Br J Ophthalmol. 2017;101(10):1352\u201360. https:\/\/doi.org\/10.1136\/bjophthalmol-2016-310002.","journal-title":"Br J Ophthalmol"},{"issue":"7","key":"1596_CR148","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1038\/s41433-020-1040-8","volume":"35","author":"TGS Martins","year":"2021","unstructured":"Martins TGS. Comment on: how is the risk of being diagnosed with referable diabetic retinopathy affected by failure to attend diabetes eye screening appointments? Eye. 2021;35(7):2045. https:\/\/doi.org\/10.1038\/s41433-020-1040-8.","journal-title":"Eye"},{"key":"1596_CR149","doi-asserted-by":"crossref","unstructured":"Rahim SS, Palade V, Holzinger A. Image Processing and Machine Learning Techniques for Diabetic Retinopathy Detection: A Review. 2020. pp. 136\u2013\u200954.","DOI":"10.1007\/978-3-030-50402-1_9"},{"key":"1596_CR150","unstructured":"Shuai M. Research status and prospect of deep learning algorithm-based artificial intelligence in assisted diagnosis of diabetic retinopathy. Chin J Experimental Ophthalmol. 2019:684\u20138."},{"key":"1596_CR151","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8841927","volume":"2020","author":"A Chu","year":"2020","unstructured":"Chu A, Squirrell D, Phillips AM, Vaghefi E. Essentials of a robust deep learning system for diabetic retinopathy screening: A systematic literature review. J Ophthalmol. 2020;2020:1\u201311. https:\/\/doi.org\/10.1155\/2020\/8841927.","journal-title":"J Ophthalmol"},{"issue":"2","key":"1596_CR152","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/nrendo.2017.177","volume":"14","author":"A Morris","year":"2018","unstructured":"Morris A. AI can diagnose diabetic retinopathy. Nat Reviews Endocrinol. 2018;14(2):65. https:\/\/doi.org\/10.1038\/nrendo.2017.177.","journal-title":"Nat Reviews Endocrinol"},{"issue":"3","key":"1596_CR153","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s40123-021-00353-2","volume":"10","author":"J Pieczynski","year":"2021","unstructured":"Pieczynski J, Kuklo P, Grzybowski A. The role of telemedicine, In-Home testing and artificial intelligence to alleviate an increasingly burdened healthcare system: diabetic retinopathy. Ophthalmol Therapy. 2021;10(3):445\u201364. https:\/\/doi.org\/10.1007\/s40123-021-00353-2.","journal-title":"Ophthalmol Therapy"},{"key":"1596_CR154","doi-asserted-by":"crossref","unstructured":"Wang L, Shang Y, Weng X, Yang X, Sang A, Dong J et al. The Development of Standardized and Sharable Computer Interpretable Guidelines for Diabetic Retinopathy. Proceedings of the 2019 International Conference on Intelligent Medicine and Health. New York, NY, USA: ACM; 2019. pp. 1\u20137.","DOI":"10.1145\/3348416.3348418"},{"issue":"5","key":"1596_CR155","doi-asserted-by":"publisher","first-page":"e182658","DOI":"10.1001\/jamanetworkopen.2018.2658","volume":"1","author":"EO Nsoesie","year":"2018","unstructured":"Nsoesie EO. Evaluating artificial intelligence applications in clinical settings. JAMA Netw Open. 2018;1(5):e182658\u2013e. https:\/\/doi.org\/10.1001\/jamanetworkopen.2018.2658.","journal-title":"JAMA Netw Open"},{"issue":"2","key":"1596_CR156","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1167\/tvst.9.2.22","volume":"9","author":"Y Xie","year":"2020","unstructured":"Xie Y, Gunasekeran DV, Balaskas K, Keane PA, Sim DA, Bachmann LM, et al. Health economic and safety considerations for artificial intelligence applications in diabetic retinopathy screening. Translational Vis Sci Technol. 2020;9(2):22. https:\/\/doi.org\/10.1167\/tvst.9.2.22.","journal-title":"Translational Vis Sci Technol"},{"issue":"11","key":"1596_CR157","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1136\/medethics-2020-107166","volume":"48","author":"T Grote","year":"2022","unstructured":"Grote T. Randomised controlled trials in medical AI: ethical considerations. J Med Ethics. 2022;48(11):899\u2013906. https:\/\/doi.org\/10.1136\/medethics-2020-107166.","journal-title":"J Med Ethics"},{"key":"1596_CR158","unstructured":"Chen J, Storchan V, Kurshan E. Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice. 2021."},{"key":"1596_CR159","doi-asserted-by":"crossref","unstructured":"Amer S. Ethical Issues of the Use of AI in Healthcare. 2021. pp. 843\u2013\u200953.","DOI":"10.1007\/978-3-030-71017-0_60"},{"key":"1596_CR160","doi-asserted-by":"crossref","unstructured":"Casas-Roma J, Conesa J, Caball\u00e9 S, Education. Ethical Dilemmas and AI: From Ethical Design to Artificial Morality. 2021. pp. 167\u2013\u200982.","DOI":"10.1007\/978-3-030-77857-6_11"},{"key":"1596_CR161","doi-asserted-by":"publisher","unstructured":"Morris MR. AI and Accessibility: A Discussion of Ethical Considerations. 2019. https:\/\/doi.org\/10.1145\/3356727","DOI":"10.1145\/3356727"},{"key":"1596_CR162","unstructured":"Ogunyemi OI, Gandhi M, Tayek C. Predictive Models for Diabetic Retinopathy from Non-Image Teleretinal Screening Data. AMIA Joint Summits on Translational Science proceedings AMIA Joint Summits on Translational Science. 2019;2019:472-7."},{"key":"1596_CR163","doi-asserted-by":"crossref","unstructured":"Verma H, Mlynar J, Schaer R, Reichenbach J, Jreige M, Prior J et al. Rethinking the Role of AI with Physicians in Oncology: Revealing Perspectives from Clinical and Research Workflows. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM; 2023. pp. 1\u201319.","DOI":"10.1145\/3544548.3581506"},{"key":"1596_CR164","doi-asserted-by":"publisher","unstructured":"Juluru K, Shih H-H, Keshava Murthy KN, Elnajjar P, El-Rowmeim A, Roth C, et al. Integrating al algorithms into the clinical workflow. Radiology: Artif Intell. 2021;3(6). https:\/\/doi.org\/10.1148\/ryai.2021210013.","DOI":"10.1148\/ryai.2021210013"},{"key":"1596_CR165","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1145\/3579375.3579412","volume-title":"2023 Australasian computer science week","author":"D Hua","year":"2023","unstructured":"Hua D, Petrina N, Young N, Cho J-G, Sacks A, Poon S. Using AI-Driven triaging to optimise clinical workflows in Non-Emergency outpatient settings. 2023 Australasian computer science week. New York, NY, USA: ACM; 2023. pp. 240\u20133."},{"issue":"4","key":"1596_CR166","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1007\/s11606-020-06394-w","volume":"36","author":"M Smith","year":"2021","unstructured":"Smith M, Sattler A, Hong G, Lin S. From code to bedside: implementing artificial intelligence using quality improvement methods. J Gen Intern Med. 2021;36(4):1061\u20136. https:\/\/doi.org\/10.1007\/s11606-020-06394-w.","journal-title":"J Gen Intern Med"},{"issue":"2","key":"1596_CR167","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1167\/tvst.9.2.20","volume":"9","author":"M Heisler","year":"2020","unstructured":"Heisler M, Karst S, Lo J, Mammo Z, Yu T, Warner S, et al. Ensemble deep learning for diabetic retinopathy detection using optical coherence tomography angiography. Translational Vis Sci Technol. 2020;9(2):20.","journal-title":"Translational Vis Sci Technol"},{"key":"1596_CR168","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vision. 2020;128:336\u201359.","journal-title":"Int J Comput Vision"},{"key":"1596_CR169","unstructured":"Garreau D, Luxburg U. Explaining the explainer: A first theoretical analysis of LIME. International conference on artificial intelligence and statistics: PMLR; 2020. pp. 1287-96."},{"issue":"23","key":"1596_CR170","doi-asserted-by":"publisher","first-page":"17429","DOI":"10.1007\/s00521-023-08608-8","volume":"35","author":"F Mercaldo","year":"2023","unstructured":"Mercaldo F, Di Giammarco M, Apicella A, Di Iadarola G, Cesarelli M, Martinelli F, Santone A. Diabetic retinopathy detection and diagnosis by means of robust and explainable convolutional neural networks. Neural Comput Appl. 2023;35(23):17429\u201341.","journal-title":"Neural Comput Appl"},{"issue":"10","key":"1596_CR171","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1001\/jamaophthalmol.2019.2923","volume":"137","author":"S Natarajan","year":"2019","unstructured":"Natarajan S, Jain A, Krishnan R, Rogye A, Sivaprasad S. Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone. JAMA Ophthalmol. 2019;137(10):1182\u20138.","journal-title":"JAMA Ophthalmol"},{"key":"1596_CR172","doi-asserted-by":"publisher","unstructured":"Ahmed Adel S. An overview of artificial intelligence use in diabetic retinopathy treatment: a narrative review. Futurity Med. 2023;4\u201313. https:\/\/doi.org\/10.57125\/FEM.2023.03.30.01.","DOI":"10.57125\/FEM.2023.03.30.01"},{"key":"1596_CR173","doi-asserted-by":"publisher","DOI":"10.1007\/s00417-023-06052-x","author":"M Daich Varela","year":"2023","unstructured":"Daich Varela M, Sen S, De Guimaraes TAC, Kabiri N, Pontikos N, Balaskas K, Michaelides M. Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefe\u2019s Archive Clin Experimental Ophthalmol. 2023. https:\/\/doi.org\/10.1007\/s00417-023-06052-x.","journal-title":"Graefe\u2019s Archive Clin Experimental Ophthalmol"},{"key":"1596_CR174","doi-asserted-by":"publisher","unstructured":"Happe M, Gill H, Salem DH, Janga SC, Hajrasouliha A. Clinical features distinguishing diabetic retinopathy severity using artificial intelligence. Proc IMPRS. 2023;5(1). https:\/\/doi.org\/10.18060\/26676.","DOI":"10.18060\/26676"},{"issue":"2","key":"1596_CR175","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1167\/tvst.10.2.13","volume":"10","author":"P Burlina","year":"2021","unstructured":"Burlina P, Joshi N, Paul W, Pacheco KD, Bressler NM. Addressing artificial intelligence bias in retinal diagnostics. Translational Vis Sci Technol. 2021;10(2):13.","journal-title":"Translational Vis Sci Technol"},{"issue":"2","key":"1596_CR176","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1038\/s41433-020-0927-8","volume":"35","author":"TW Rogers","year":"2021","unstructured":"Rogers TW, Gonzalez-Bueno J, Garcia Franco R, Lopez Star E, M\u00e9ndez Mar\u00edn D, Vassallo J, et al. Evaluation of an AI system for the detection of diabetic retinopathy from images captured with a handheld portable fundus camera: the MAILOR AI study. Eye. 2021;35(2):632\u20138.","journal-title":"Eye"},{"issue":"6","key":"1596_CR177","doi-asserted-by":"publisher","first-page":"e1560","DOI":"10.1002\/widm.1560","volume":"14","author":"M Abdollahi","year":"2024","unstructured":"Abdollahi M, Jafarizadeh A, Ghafouri-Asbagh A, Sobhi N, Pourmoghtader K, Pedrammehr S, et al. Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade. WIREs Data Min Knowl Discov. 2024;14(6):e1560. https:\/\/doi.org\/10.1002\/widm.1560.","journal-title":"WIREs Data Min Knowl Discov"},{"key":"1596_CR178","doi-asserted-by":"publisher","unstructured":"Nolan BM, DayBranch ER, Barton K, Korsen N. Patient and provider experience with artificial intelligence screening technology for diabetic retinopathy in a rural primary care setting. J Maine Med Cent. 2023;5(2). https:\/\/doi.org\/10.46804\/2641-2225.1144.","DOI":"10.46804\/2641-2225.1144"},{"issue":"10","key":"1596_CR179","doi-asserted-by":"publisher","first-page":"3537","DOI":"10.3390\/jcm12103537","volume":"12","author":"MR Land","year":"2023","unstructured":"Land MR, Patel PA, Bui T, Jiao C, Ali A, Ibnamasud S, et al. Examining the role of telemedicine in diabetic retinopathy. J Clin Med. 2023;12(10):3537. https:\/\/doi.org\/10.3390\/jcm12103537.","journal-title":"J Clin Med"},{"issue":"9","key":"1596_CR180","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1089\/tmj.2022.0357","volume":"29","author":"JM Bryan","year":"2023","unstructured":"Bryan JM, Bryar PJ, Mirza RG. Convolutional neural networks accurately identify ungradable images in a diabetic retinopathy telemedicine screening program. Telemedicine e-Health. 2023;29(9):1349\u201355. https:\/\/doi.org\/10.1089\/tmj.2022.0357.","journal-title":"Telemedicine e-Health"},{"key":"1596_CR181","doi-asserted-by":"publisher","first-page":"e41624","DOI":"10.2196\/41624","volume":"9","author":"S Lin","year":"2023","unstructured":"Lin S, Ma Y, Xu Y, Lu L, He J, Zhu J, et al. Artificial intelligence in Community-Based diabetic retinopathy telemedicine screening in urban China: Cost-effectiveness and Cost-Utility analyses with Real-world data. JMIR Public Health Surveillance. 2023;9:e41624\u2013e. https:\/\/doi.org\/10.2196\/41624.","journal-title":"JMIR Public Health Surveillance"},{"key":"1596_CR182","first-page":"1808","volume-title":"Ensemble learning for disease prediction: A review","author":"P Mahajan","year":"2023","unstructured":"Mahajan P, Uddin S, Hajati F, Moni MA. Ensemble learning for disease prediction: A review. Healthcare: MDPI; 2023. p. 1808."},{"key":"1596_CR183","doi-asserted-by":"crossref","unstructured":"Shree R, Madagaonkar S, Prateek LA, Tony A, Rathnamma M, Venkata Ramana V, Chandrasekaran K. Application of Ensemble Methods in Medical Diagnosis. International Conference on Innovations in Data Analytics: Springer; 2022. pp. 355\u2013\u200967.","DOI":"10.1007\/978-981-99-0550-8_29"},{"issue":"1","key":"1596_CR184","doi-asserted-by":"publisher","first-page":"124","DOI":"10.3390\/diagnostics13010124","volume":"13","author":"SS Mondal","year":"2022","unstructured":"Mondal SS, Mandal N, Singh KK, Singh A, Izonin I. Edldr: an ensemble deep learning technique for detection and classification of diabetic retinopathy. Diagnostics. 2022;13(1):124.","journal-title":"Diagnostics"},{"issue":"2","key":"1596_CR185","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1134\/S1054661824700123","volume":"34","author":"MM Lukashevich","year":"2024","unstructured":"Lukashevich MM. Diabetic retinopathy fundus image classification using ensemble methods. Pattern Recognit Image Anal. 2024;34(2):331\u20139.","journal-title":"Pattern Recognit Image Anal"},{"key":"1596_CR186","doi-asserted-by":"crossref","unstructured":"Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011\u20132022). Computer Methods and Programs in Biomedicine. 2022:107161.","DOI":"10.1016\/j.cmpb.2022.107161"},{"key":"1596_CR187","doi-asserted-by":"publisher","unstructured":"Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. 2017. https:\/\/doi.org\/10.48550\/arXiv.1702.08608","DOI":"10.48550\/arXiv.1702.08608"},{"key":"1596_CR188","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C. Why should i trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining2016. pp. 1135-44.","DOI":"10.1145\/2939672.2939778"},{"issue":"26","key":"1596_CR189","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1056\/NEJMp1702071","volume":"376","author":"JH Chen","year":"2017","unstructured":"Chen JH, Asch SM. Machine learning and prediction in medicine\u2014beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507.","journal-title":"N Engl J Med"},{"issue":"1","key":"1596_CR190","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/s13755-018-0054-0","volume":"6","author":"PM Shakeel","year":"2018","unstructured":"Shakeel PM, Baskar S, Dhulipala VRS, Jaber MM. Cloud based framework for diagnosis of diabetes mellitus using K-means clustering. Health Inform Sci Syst. 2018;6(1):16. https:\/\/doi.org\/10.1007\/s13755-018-0054-0.","journal-title":"Health Inform Sci Syst"},{"key":"1596_CR191","doi-asserted-by":"publisher","first-page":"32804","DOI":"10.1109\/ACCESS.2023.3258549","volume":"11","author":"SK Sharma","year":"2023","unstructured":"Sharma SK, Zamani AT, Abdelsalam A, Muduli D, Alabrah AA, Parveen N, Alanazi SM. A diabetes monitoring system and Health-Medical service composition model in cloud environment. IEEE Access. 2023;11:32804\u201319. https:\/\/doi.org\/10.1109\/ACCESS.2023.3258549.","journal-title":"IEEE Access"},{"issue":"6","key":"1596_CR192","doi-asserted-by":"publisher","first-page":"e18167","DOI":"10.2196\/18167","volume":"23","author":"R Salari","year":"2021","unstructured":"Salari R, Niakan Kalhori R, GhaziSaeedi S, Jeddi M, Nazari M, Fatehi M. Mobile-Based and Cloud-Based system for Self-management of people with type 2 diabetes: development and usability evaluation. J Med Internet Res. 2021;23(6):e18167\u2013e. https:\/\/doi.org\/10.2196\/18167.","journal-title":"J Med Internet Res"},{"issue":"21","key":"1596_CR193","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.3390\/electronics10212719","volume":"10","author":"AR Nasser","year":"2021","unstructured":"Nasser AR, Hasan AM, Humaidi AJ, Alkhayyat A, Alzubaidi L, Fadhel MA, et al. IoT and cloud computing in Health-Care: A new wearable device and cloud-Based deep learning algorithm for monitoring of diabetes. Electronics. 2021;10(21):2719. https:\/\/doi.org\/10.3390\/electronics10212719.","journal-title":"Electronics"}],"container-title":["Journal of Diabetes &amp; Metabolic Disorders"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40200-025-01596-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40200-025-01596-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40200-025-01596-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:07:32Z","timestamp":1757156852000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40200-025-01596-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,12]]},"references-count":193,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["1596"],"URL":"https:\/\/doi.org\/10.1007\/s40200-025-01596-7","relation":{"references":[{"id-type":"doi","id":"10.1167\/iovs.16-19964","asserted-by":"subject"}]},"ISSN":["2251-6581"],"issn-type":[{"value":"2251-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,12]]},"assertion":[{"value":"3 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was conducted under the principles outlined in the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"N\/A.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that there is no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"104"}}