{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T20:17:23Z","timestamp":1772050643665,"version":"3.50.1"},"reference-count":28,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000053","name":"National Eye Institute","doi-asserted-by":"publisher","award":["K23EY029246"],"award-info":[{"award-number":["K23EY029246"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["OT2OD032644"],"award-info":[{"award-number":["OT2OD032644"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018550","name":"Lowy Medical Research Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018550","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100030855","name":"Microsoft AI","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100030855","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["U19AG066567"],"award-info":[{"award-number":["U19AG066567"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001818","name":"Research to Prevent Blindness","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001818","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ophthalmologyscience.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Ophthalmology Science"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1016\/j.xops.2025.100710","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T02:52:34Z","timestamp":1736736754000},"page":"100710","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhanced Macular Telangiectasia Type 2 Detection: Leveraging Self-Supervised Learning and Ensemble Models"],"prefix":"10.1016","volume":"5","author":[{"given":"Shahrzad","family":"Gholami","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lea","family":"Scheppke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meghana","family":"Kshirsagar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahul","family":"Dodhia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Bonelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Irene","family":"Leung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ferenc B.","family":"Sallo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alyson","family":"Muldrew","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catherine","family":"Jamison","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tunde","family":"Peto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan Lavista","family":"Ferres","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William B.","family":"Weeks","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Friedlander","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaron Y.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.xops.2025.100710_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.xops.2022.100261","article-title":"Macular telangiectasia type 2: a classification system using MultiModal imaging MacTel Project report number 10","volume":"3","author":"Chew","year":"2023","journal-title":"Ophthalmol Sci"},{"key":"10.1016\/j.xops.2025.100710_bib2","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.oret.2016.12.009","article-title":"Deep learning is effective for classifying normal versus age-related macular degeneration OCT images","volume":"1","author":"Lee","year":"2017","journal-title":"Ophthalmology Retina"},{"key":"10.1016\/j.xops.2025.100710_bib3","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br J Ophthalmol"},{"key":"10.1016\/j.xops.2025.100710_bib4","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"De Fauw","year":"2018","journal-title":"Nat Med"},{"key":"10.1016\/j.xops.2025.100710_bib5","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1136\/bjophthalmol-2020-317131","article-title":"Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2","volume":"106","author":"Loo","year":"2022","journal-title":"Br J Ophthalmol"},{"key":"10.1016\/j.xops.2025.100710_bib6","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.preteyeres.2012.11.002","article-title":"Macular telangiectasia type 2","volume":"34","author":"Charbel Issa","year":"2013","journal-title":"Prog Retin Eye Res"},{"key":"10.1016\/j.xops.2025.100710_bib7","first-page":"2045","article-title":"An interpretable ensemble deep learning model for diabetic retinopathy disease classification","volume":"2019","author":"Jiang","year":"2019","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"10.1016\/j.xops.2025.100710_bib8","doi-asserted-by":"crossref","first-page":"96946","DOI":"10.1109\/ACCESS.2020.2993536","article-title":"Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis","volume":"8","author":"Zheng","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.xops.2025.100710_bib9","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1023\/A:1018054314350","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach Learn"},{"key":"10.1016\/j.xops.2025.100710_bib10","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1023\/A:1022648800760","article-title":"The strength of weak learnability","volume":"5","author":"Schapire","year":"1990","journal-title":"Mach Learn"},{"key":"10.1016\/j.xops.2025.100710_bib11","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1038\/s41467-021-21466-z","article-title":"Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images","volume":"12","author":"Zhou","year":"2021","journal-title":"Nat Commun"},{"key":"10.1016\/j.xops.2025.100710_bib12","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1001\/jamaophthalmol.2023.6454","article-title":"Self-supervised learning for improved optical coherence tomography detection of macular telangiectasia type 2","volume":"142","author":"Gholami","year":"2024","journal-title":"JAMA Ophthalmol"},{"key":"10.1016\/j.xops.2025.100710_bib13","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3109\/09286580903450361","article-title":"Baseline characteristics of participants in the natural history study of macular telangiectasia (MacTel) MacTel Project Report No. 2","volume":"17","author":"Clemons","year":"2010","journal-title":"Ophthalmic Epidemiol"},{"key":"10.1016\/j.xops.2025.100710_bib14","series-title":"International Conference on Machine Learning","first-page":"148","article-title":"Experiments with a new boosting algorithm","author":"Freund","year":"1996"},{"key":"10.1016\/j.xops.2025.100710_bib15","author":"Freund"},{"key":"10.1016\/j.xops.2025.100710_bib16","doi-asserted-by":"crossref","first-page":"2790","DOI":"10.1038\/s41467-022-30459-5","article-title":"A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology","volume":"13","author":"Zheng","year":"2022","journal-title":"Nat Commun"},{"key":"10.1016\/j.xops.2025.100710_bib17","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1613\/jair.614","article-title":"Popular ensemble methods: an empirical study","volume":"11","author":"Opitz","year":"1999","journal-title":"Jair"},{"key":"10.1016\/j.xops.2025.100710_bib18","series-title":"Combining Pattern Classifiers: Methods and Algorithms","author":"Kuncheva","year":"2014"},{"key":"10.1016\/j.xops.2025.100710_bib19","series-title":"2017 IEEE International Conference on Computer Vision (ICCV)","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","author":"Selvaraju","year":"2017"},{"key":"10.1016\/j.xops.2025.100710_bib20","article-title":"DeepOCT: an explainable deep learning architecture to analyze macular edema on OCT images","volume":"34","author":"Altan","year":"2022","journal-title":"Eng Sci Technol Int J"},{"key":"10.1016\/j.xops.2025.100710_bib21","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A survey on explainable artificial intelligence (XAI): toward medical XAI","volume":"32","author":"Tjoa","year":"2021","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.xops.2025.100710_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable artificial intelligence (XAI) in deep learning-based medical image analysis","volume":"79","author":"van der Velden","year":"2022","journal-title":"Med Image Anal"},{"key":"10.1016\/j.xops.2025.100710_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110273","article-title":"Explainable AI (XAI): a systematic meta-survey of current challenges and future opportunities","volume":"263","author":"Saeed","year":"2023","journal-title":"Knowl Base Syst"},{"key":"10.1016\/j.xops.2025.100710_bib24","author":"Pang"},{"key":"10.1016\/j.xops.2025.100710_bib25","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.media.2015.08.001","article-title":"Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box","volume":"26","author":"Ciompi","year":"2015","journal-title":"Med Image Anal"},{"key":"10.1016\/j.xops.2025.100710_bib26","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.media.2017.01.008","article-title":"Deep ensemble learning of sparse regression models for brain disease diagnosis","volume":"37","year":"2017","journal-title":"Med Image Anal"},{"key":"10.1016\/j.xops.2025.100710_bib27","article-title":"Deep ensemble learning for Alzheimer's disease classification","volume":"105","year":"2020","journal-title":"J Biomed Inf"},{"key":"10.1016\/j.xops.2025.100710_bib28","doi-asserted-by":"crossref","DOI":"10.3389\/fmed.2024.1438768","article-title":"Dry age-related macular degeneration classification from optical coherence tomography images based on ensemble deep learning architecture","volume":"11","author":"Yang","year":"2024","journal-title":"Front Med"}],"container-title":["Ophthalmology Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666914525000089?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666914525000089?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T19:47:24Z","timestamp":1772048844000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666914525000089"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":28,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["S2666914525000089"],"URL":"https:\/\/doi.org\/10.1016\/j.xops.2025.100710","relation":{},"ISSN":["2666-9145"],"issn-type":[{"value":"2666-9145","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhanced Macular Telangiectasia Type 2 Detection: Leveraging Self-Supervised Learning and Ensemble Models","name":"articletitle","label":"Article Title"},{"value":"Ophthalmology Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.xops.2025.100710","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 by the American Academy of Ophthalmology.","name":"copyright","label":"Copyright"}],"article-number":"100710"}}