{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T08:10:00Z","timestamp":1778314200425,"version":"3.51.4"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:00:00Z","timestamp":1778284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:00:00Z","timestamp":1778284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003483","name":"Hebrew University of Jerusalem","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003483","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Graefes Arch Clin Exp Ophthalmol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Age-related macular degeneration (AMD), a leading cause of visual impairment and blindness among the elderly, is projected to affect 288\u00a0million individuals globally by 2040. Advanced AMD, including complete retinal pigment epithelium and outer retinal atrophy (cRORA), pose significant challenges for diagnosis and monitoring due to the labor-intensive, costly, and variable nature of manual annotation of volumetric optical coherence tomography (OCT) scans. Automating cRORA diagnosis offers the potential to improve annotation consistency and reduce clinical burden, which could facilitate, for example, the evaluation of recently FDA-approved treatments that delay disease progression. In this study, we compiled two large independent cohorts totaling nearly 5,000 3D OCT scans, labeled them for cRORA presence, and developed a deep learning model for cRORA automated detection. The model achieved state-of-the-art performance, with a ROC AUC of 0.97 on internal validation, and demonstrated robust translatability (zero-shot learning) with a ROC AUC of 0.88 on external evaluation. Notably, it exhibited high accuracy for both non-neovascular (non-nv) and neovascular (nv) AMD subgroups (ROC AUC 0.98 and 0.93, respectively), including complex cases with exudation. This model and dataset combination could facilitate clinical research and trial analyses by providing scalable, standardized assessments across non-nv and nv AMD patient subgroups.<\/jats:p>","DOI":"10.1007\/s00417-026-07205-4","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T07:17:52Z","timestamp":1778311072000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A deep learning model for automated identification of age-related macular degeneration atrophy"],"prefix":"10.1007","author":[{"given":"Oren","family":"Avram","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahel","family":"Shwartz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adi","family":"Green","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rivka","family":"Bloom","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giulia","family":"Corradetti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthony","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyuan Johnson","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tal Eshkoly","family":"Lior","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Berkin","family":"Durmus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akos","family":"Rudas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravi","family":"Pal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadav","family":"Rakocz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ceren","family":"Soylu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mai","family":"Alhelaly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giacomo","family":"Boscia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charles C.","family":"Wykoff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maxime","family":"Cannesson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Srinivas R.","family":"Sadda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaime","family":"Levy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eran","family":"Halperin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey N.","family":"Chiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Itay","family":"Chowers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liran","family":"Tiosano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,9]]},"reference":[{"key":"7205_CR1","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:e106\u2013e116","journal-title":"Lancet Glob Health"},{"key":"7205_CR2","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1016\/j.ophtha.2017.09.028","volume":"125","author":"SR Sadda","year":"2018","unstructured":"Sadda SR, Guymer R, Holz FG, Schmitz-Valckenberg S, Curcio CA, Bird AC et al (2018) Consensus Definition for Atrophy Associated with Age-Related Macular Degeneration on OCT: Classification of Atrophy Report 3. Ophthalmology 125:537\u2013548","journal-title":"Ophthalmology"},{"key":"7205_CR3","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1016\/j.ophtha.2017.12.026","volume":"125","author":"SR Sadda","year":"2018","unstructured":"Sadda SR, Tuomi LL, Ding B, Fung AE, Hopkins JJ (2018) Macular atrophy in the HARBOR study for neovascular age-related macular degeneration. Ophthalmology 125:878\u2013886","journal-title":"Ophthalmology"},{"key":"7205_CR4","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.ophtha.2016.10.002","volume":"124","author":"NS Abdelfattah","year":"2017","unstructured":"Abdelfattah NS, Al-Sheikh M, Pitetta S, Mousa A, Sadda SR, Wykoff CC et al (2017) Macular atrophy in neovascular age-related macular degeneration with monthly versus treat-and-extend ranibizumab: Findings from the TREX-AMD trial. Ophthalmology 124:215\u2013223","journal-title":"Ophthalmology"},{"key":"7205_CR5","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.ophtha.2019.09.035","volume":"127","author":"RH Guymer","year":"2020","unstructured":"Guymer RH, Rosenfeld PJ, Curcio CA, Holz FG, Staurenghi G, Freund KB et al (2020) Incomplete retinal pigment epithelial and outer retinal atrophy in age-related macular degeneration: Classification of Atrophy Meeting report 4. Ophthalmology 127:394\u2013409","journal-title":"Ophthalmology"},{"key":"7205_CR6","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.ophtha.2016.12.002","volume":"124","author":"FG Holz","year":"2017","unstructured":"Holz FG, Sadda SR, Staurenghi G, Lindner M, Bird AC, Blodi BA et al (2017) Imaging protocols in clinical studies in advanced age-related macular degeneration: Recommendations from Classification of atrophy consensus meetings. Ophthalmology 124:464\u2013478","journal-title":"Ophthalmology"},{"key":"7205_CR7","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1080\/14737159.2023.2208751","volume":"23","author":"W Wei","year":"2023","unstructured":"Wei W, Anantharanjit R, Patel RP, Cordeiro MF (2023) Detection of macular atrophy in age-related macular degeneration aided by artificial intelligence. Expert Rev Mol Diagn 23:485\u2013494","journal-title":"Expert Rev Mol Diagn"},{"key":"7205_CR8","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","volume":"28","author":"P Rajpurkar","year":"2022","unstructured":"Rajpurkar P, Chen E, Banerjee O, Topol EJ (2022) AI in health and medicine. Nat Med 28:31\u201338","journal-title":"Nat Med"},{"key":"7205_CR9","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.oret.2022.08.016","volume":"7","author":"JN Chiang","year":"2023","unstructured":"Chiang JN, Corradetti G, Nittala MG, Corvi F, Rakocz N, Rudas A et al (2023) Automated Identification of Incomplete and Complete Retinal Epithelial Pigment and Outer Retinal Atrophy Using Machine Learning. Ophthalmol Retina 7:118\u2013126","journal-title":"Ophthalmol Retina"},{"key":"7205_CR10","doi-asserted-by":"publisher","first-page":"2732","DOI":"10.1364\/BOE.8.002732","volume":"8","author":"L Fang","year":"2017","unstructured":"Fang L, Cunefare D, Wang C, Guymer RH, Li S, Farsiu S (2017) Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Express 8:2732\u20132744","journal-title":"Biomed Opt Express"},{"key":"7205_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1167\/tvst.7.1.1","volume":"7","author":"Z Ji","year":"2018","unstructured":"Ji Z, Chen Q, Niu S, Leng T, Rubin DL (2018) Beyond retinal layers: A deep voting model for automated geographic atrophy segmentation in SD-OCT images. Transl Vis Sci Technol 7:1","journal-title":"Transl Vis Sci Technol"},{"key":"7205_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ajo.2020.12.034","volume":"226","author":"B Liefers","year":"2021","unstructured":"Liefers B, Taylor P, Alsaedi A, Bailey C, Balaskas K, Dhingra N et al (2021) Quantification of key retinal features in early and late age-related macular degeneration using deep learning. Am J Ophthalmol 226:1\u201312","journal-title":"Am J Ophthalmol"},{"key":"7205_CR13","doi-asserted-by":"crossref","unstructured":"Avram O, Durmus B, Rakocz N, Corradetti G, An U, Nittala MG et al (2025) Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans. Nat Biomedical Engineering 1\u201314","DOI":"10.1038\/s41551-024-01257-9"},{"key":"7205_CR14","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41746-021-00411-w","volume":"4","author":"N Rakocz","year":"2021","unstructured":"Rakocz N, Chiang JN, Nittala MG, Corradetti G, Tiosano L, Velaga S et al (2021) Automated identification of clinical features from sparsely annotated 3-dimensional medical imaging. NPJ Digit Med 4:44","journal-title":"NPJ Digit Med"},{"key":"7205_CR15","doi-asserted-by":"crossref","unstructured":"Tran D, Wang H, Torresani L, Ray J, LeCun Y, Paluri M (2017) A Closer Look at Spatiotemporal Convolutions for Action Recognition. arXiv [cs.CV]. Available: http:\/\/arxiv.org\/abs\/1711.11248","DOI":"10.1109\/CVPR.2018.00675"},{"key":"7205_CR16","doi-asserted-by":"publisher","first-page":"eabo4802","DOI":"10.1126\/scitranslmed.abo4802","volume":"14","author":"J Witowski","year":"2022","unstructured":"Witowski J, Heacock L, Reig B, Kang SK, Lewin A, Pysarenko K et al (2022) Improving breast cancer diagnostics with deep learning for MRI. Sci Transl Med 14:eabo4802","journal-title":"Sci Transl Med"},{"key":"7205_CR17","doi-asserted-by":"crossref","unstructured":"Turnbull R (2023) Using a 3D ResNet for Detecting the Presence and Severity of COVID-19 from CT Scans. Computer Vision \u2013 ECCV 2022 Workshops. Springer Nature Switzerland, pp 663\u2013676","DOI":"10.1007\/978-3-031-25082-8_45"},{"key":"7205_CR18","doi-asserted-by":"publisher","unstructured":"Zou Q, Huang Y, Gao J, Zhang B, Wang D, Wan M (2023) Three-dimensional ultrasound image reconstruction based on 3D-ResNet in the musculoskeletal system using a 1D probe:ex vivoandin vivofeasibility studies. Phys Med Biol 68. https:\/\/doi.org\/10.1088\/1361-6560\/ace58b","DOI":"10.1088\/1361-6560\/ace58b"},{"key":"7205_CR19","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 CCS, Liang H, Baxter SL et al (2018) Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell 172:1122\u20131131e9","journal-title":"Cell"},{"key":"7205_CR20","unstructured":"Cho J, Lee K, Shin E, Choy G, Do S (2015) How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv [cs.LG]. Available: http:\/\/arxiv.org\/abs\/1511.06348"},{"key":"7205_CR21","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1037\/h0031619","volume":"76","author":"JL Fleiss","year":"1971","unstructured":"Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76:378\u2013382","journal-title":"Psychol Bull"},{"key":"7205_CR22","doi-asserted-by":"publisher","first-page":"9994098","DOI":"10.1155\/2021\/9994098","volume":"2021","author":"C Metrangolo","year":"2021","unstructured":"Metrangolo C, Donati S, Mazzola M, Fontanel L, Messina W, D\u2019alterio G et al (2021) OCT biomarkers in neovascular age-related macular degeneration: A narrative review. J Ophthalmol 2021:9994098","journal-title":"J Ophthalmol"},{"key":"7205_CR23","doi-asserted-by":"publisher","first-page":"6886","DOI":"10.1167\/iovs.13-11665","volume":"54","author":"G Querques","year":"2013","unstructured":"Querques G, Srour M, Massamba N, Georges A, Ben Moussa N, Rafaeli O et al (2013) Functional characterization and multimodal imaging of treatment-naive quiescent choroidal neovascularization. Invest Ophthalmol Vis Sci 54:6886\u20136892","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"7205_CR24","doi-asserted-by":"publisher","first-page":"3164","DOI":"10.1177\/1120672120986370","volume":"31","author":"G Querques","year":"2021","unstructured":"Querques G, Sacconi R, Capuano V, Carnevali A, Colantuono D, Battista M et al (2021) Treatment-na\u00efve quiescent macular neovascularization secondary to AMD: The 2019 Young Investigator Lecture of Macula Society. Eur J Ophthalmol 31:3164\u20133176","journal-title":"Eur J Ophthalmol"},{"key":"7205_CR25","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1016\/j.ophtha.2012.10.036","volume":"120","author":"FL Ferris 3rd","year":"2013","unstructured":"Ferris FL 3rd, Wilkinson CP, Bird A, Chakravarthy U, Chew E, Csaky K et al (2013) Clinical classification of age-related macular degeneration. Ophthalmology 120:844\u2013851","journal-title":"Ophthalmology"},{"key":"7205_CR26","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T et al (2020) An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv [cs.CV]. Available: http:\/\/arxiv.org\/abs\/2010.11929"},{"key":"7205_CR27","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN et al (2017) Attention is all you need. arXiv [cs.CL]. Available: http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"7205_CR28","unstructured":"Hendrycks D, Gimpel K Gaussian Error Linear Units (GELUs). arXiv [cs.LG]. 2016. Available: http:\/\/arxiv.org\/abs\/1606.08415"},{"key":"7205_CR29","unstructured":"Smith LN (2015) Cyclical Learning Rates for Training Neural Networks. arXiv [cs.CV]. Available: http:\/\/arxiv.org\/abs\/1506.01186"},{"key":"7205_CR30","unstructured":"Smith LN, Topin N, Super-Convergence (2017) Very Fast Training of Neural Networks Using Large Learning Rates. arXiv [cs.LG]. Available: http:\/\/arxiv.org\/abs\/1708.07120"},{"key":"7205_CR31","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","volume":"1","author":"A Rajkomar","year":"2018","unstructured":"Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M et al (2018) Scalable and accurate deep learning with electronic health records. NPJ Digit Med 1:18","journal-title":"NPJ Digit Med"},{"key":"7205_CR32","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.1038\/s41551-022-00936-9","volume":"6","author":"E Tiu","year":"2022","unstructured":"Tiu E, Talius E, Patel P, Langlotz CP, Ng AY, Rajpurkar P (2022) Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning. Nat Biomed Eng 6:1399\u20131406","journal-title":"Nat Biomed Eng"},{"key":"7205_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.preteyeres.2018.07.004","volume":"67","author":"U Schmidt-Erfurth","year":"2018","unstructured":"Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunovi\u0107 H (2018) Artificial intelligence in retina. Prog Retin Eye Res 67:1\u201329","journal-title":"Prog Retin Eye Res"},{"key":"7205_CR34","doi-asserted-by":"publisher","first-page":"21893","DOI":"10.1038\/s41598-021-01227-0","volume":"11","author":"Y Derradji","year":"2021","unstructured":"Derradji Y, Mosinska A, Apostolopoulos S, Ciller C, De Zanet S, Mantel I (2021) Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography. Sci Rep 11:21893","journal-title":"Sci Rep"},{"key":"7205_CR35","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.compbiomed.2018.12.013","volume":"105","author":"R Xu","year":"2019","unstructured":"Xu R, Niu S, Chen Q, Ji Z, Rubin D, Chen Y (2019) Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning model. Comput Biol Med 105:102\u2013111","journal-title":"Comput Biol Med"}],"container-title":["Graefe's Archive for Clinical and Experimental Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00417-026-07205-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00417-026-07205-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00417-026-07205-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T07:18:00Z","timestamp":1778311080000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00417-026-07205-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,9]]},"references-count":35,"alternative-id":["7205"],"URL":"https:\/\/doi.org\/10.1007\/s00417-026-07205-4","relation":{},"ISSN":["0721-832X","1435-702X"],"issn-type":[{"value":"0721-832X","type":"print"},{"value":"1435-702X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,9]]},"assertion":[{"value":"19 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"S.R.S. has affiliations with Abbvie\/Allergan, Alexion, Amgen, Apellis, ARVO, Astellas, Bayer, Biogen, Boerhinger Ingelheim, Carl Zeiss Meditec, Centervue, Character, Eyepoint, Heidelberg, iCare, IvericBio, Jannsen, Macula Society, Nanoscope, Nidek, NotalVision, Novartis, Optos, OTx, Pfizer, Regeneron, Roche, Samsung Bioepis, Topcon.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"S.R.S. serves as an Editor-in-Chief of Graefe\u2019s Archive for Clinical and Experimental Ophthalmology and has affiliations with Abbvie\/Allergan, Alexion, Amgen, Apellis, ARVO, Astellas, Bayer, Biogen, Boerhinger Ingelheim, Carl Zeiss Meditec, Centervue, Character, Eyepoint, Heidelberg, iCare, IvericBio, Jannsen, Macula Society, Nanoscope, Nidek, NotalVision, Novartis, Optos, OTx, Pfizer, Regeneron, Roche, Samsung Bioepis, Topcon.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This research obtained ethical approval from the institutional review boards (IRBs) of the Hadassah Medical Center (protocol #:382-19). The study adhered to the ethical principles outlined in the declaration of Helsinki. All imaging and data underwent a rigorous de- identification process before being analyzed. The Houston dataset was scanned at the Retina Consultants of Texas Eye Clinics and analyzed at the Doheny Image Reading Research Laboratory (DIRRL) in compliance with the Declaration of Helsinki and was approved by the Ethics Committee of Retina Consultants Texas (Houston Methodist Hospital, Pro00020661:1 \u201cRetrospective Prospective Analysis of Retinal Diseases\u201d) and the UCLA Institutional Review Board (IRB, Ocular Imaging Study, Doheny Eye Center UCLA). Clinical trial number: not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Institutional Review Board (IRB)"}}]}}