{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T03:24:07Z","timestamp":1787973847198,"version":"build-2784847793"},"reference-count":27,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T00:00:00Z","timestamp":1736294400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21EY033480"],"award-info":[{"award-number":["R21EY033480"]}],"id":[{"id":"10.13039\/100000002","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":[[2025,5]]},"DOI":"10.1016\/j.xops.2025.100707","type":"journal-article","created":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T02:44:20Z","timestamp":1736563460000},"page":"100707","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"title":["How Foundational Is the Retina Foundation Model? Estimating RETFound\u2019s Label Efficiency on Binary Classification of Normal versus Abnormal OCT Images"],"prefix":"10.1016","volume":"5","author":[{"given":"David","family":"Kuo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qitong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dev","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miroslav","family":"Pajic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4676-1844","authenticated-orcid":false,"given":"Majda","family":"Hadziahmetovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.xops.2025.100707_bib1","doi-asserted-by":"crossref","first-page":"1952","DOI":"10.1016\/S0161-6420(03)00817-0","article-title":"Longitudinal rates of annual eye examinations of persons with diabetes and chronic eye diseases","volume":"110","author":"Lee","year":"2003","journal-title":"Ophthalmology"},{"key":"10.1016\/j.xops.2025.100707_bib2","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.2337\/dc13-1038","article-title":"Racial disparity of eye examinations among the U.S. Working-age population with diabetes: 2002\u20132009","volume":"37","author":"Shi","year":"2014","journal-title":"Diabetes Care"},{"key":"10.1016\/j.xops.2025.100707_bib3","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1001\/jamaophthalmol.2017.1150","article-title":"Evaluation of diabetic retinal screening and factors for ophthalmology referral in a telemedicine network","volume":"135","author":"Jani","year":"2017","journal-title":"JAMA Ophthalmol"},{"key":"10.1016\/j.xops.2025.100707_bib4","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"10.1016\/j.xops.2025.100707_bib5","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"10.1016\/j.xops.2025.100707_bib6","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"1","author":"Abr\u00e0moff","year":"2018","journal-title":"npj Digital Med"},{"key":"10.1016\/j.xops.2025.100707_bib7","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1001\/jamaophthalmol.2019.2004","article-title":"Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India","volume":"137","author":"Gulshan","year":"2019","journal-title":"JAMA Ophthalmol"},{"key":"10.1016\/j.xops.2025.100707_bib8","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s41746-019-0099-8","article-title":"Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program","volume":"2","author":"Ruamviboonsuk","year":"2019","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.xops.2025.100707_bib9","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1136\/bjophthalmol-2020-316594","article-title":"Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients","volume":"105","author":"Heydon","year":"2020","journal-title":"Br J Ophthalmol"},{"key":"10.1016\/j.xops.2025.100707_bib10","doi-asserted-by":"crossref","first-page":"e235","DOI":"10.1016\/S2589-7500(22)00017-6","article-title":"Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study","volume":"4","author":"Ruamviboonsuk","year":"2022","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.xops.2025.100707_bib11","author":"Bommasani"},{"key":"10.1016\/j.xops.2025.100707_bib12","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020"},{"key":"10.1016\/j.xops.2025.100707_bib13","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","article-title":"A foundation model for generalizable disease detection from retinal images","volume":"622","author":"Zhou","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.xops.2025.100707_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101561","article-title":"IDRiD: diabetic retinopathy \u2013 segmentation and grading challenge","volume":"59","author":"Porwal","year":"2020","journal-title":"Med Image Anal"},{"key":"10.1016\/j.xops.2025.100707_bib16","doi-asserted-by":"crossref","first-page":"231","DOI":"10.5566\/ias.1155","article-title":"Feedback on a publicly distributed database: the Messidor database","volume":"33","author":"Decenci\u00e8re","year":"2014","journal-title":"Image Anal Stereol"},{"key":"10.1016\/j.xops.2025.100707_bib17","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.xops.2025.100707_bib18","article-title":"Resnet strikes back: an improved training procedure in timm","author":"Wightman","year":"2021","journal-title":"arXiv"},{"key":"10.1016\/j.xops.2025.100707_bib19","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv"},{"key":"10.1016\/j.xops.2025.100707_bib20","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"10012","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.xops.2025.100707_bib21","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"9650","article-title":"Emerging properties in self-supervised vision transformers","author":"Caron","year":"2021"},{"key":"10.1016\/j.xops.2025.100707_bib22","article-title":"Beit: bert pre-training of image transformers","author":"Bao","year":"2021","journal-title":"arXiv"},{"key":"10.1016\/j.xops.2025.100707_bib23","series-title":"European Conference on Computer Vision","first-page":"516","article-title":"Deit iii: revenge of the vit","author":"Touvron","year":"2022"},{"key":"10.1016\/j.xops.2025.100707_bib24","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16000","article-title":"Masked autoencoders are scalable vision learners","author":"He","year":"2022"},{"key":"10.1016\/j.xops.2025.100707_bib25","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","first-page":"702","article-title":"Randaugment: practical automated data augmentation with a reduced search space","author":"Cubuk","year":"2020"},{"key":"10.1016\/j.xops.2025.100707_bib26","article-title":"Large batch training of convolutional networks","author":"You","year":"2017","journal-title":"arXiv"},{"key":"10.1016\/j.xops.2025.100707_bib27","article-title":"ConvNets match vision transformers at scale","author":"Smith","year":"2023","journal-title":"arXiv"},{"key":"10.1016\/j.xops.2025.100707_bib28","article-title":"Battle of the backbones: a large-scale comparison of pretrained models across computer vision tasks","author":"Goldblum","year":"2023","journal-title":"arXiv"}],"container-title":["Ophthalmology Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666914525000053?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666914525000053?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T19:44:42Z","timestamp":1772048682000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666914525000053"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":27,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["S2666914525000053"],"URL":"https:\/\/doi.org\/10.1016\/j.xops.2025.100707","relation":{},"ISSN":["2666-9145"],"issn-type":[{"value":"2666-9145","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"How Foundational Is the Retina Foundation Model? Estimating RETFound\u2019s Label Efficiency on Binary Classification of Normal versus Abnormal OCT Images","name":"articletitle","label":"Article Title"},{"value":"Ophthalmology Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.xops.2025.100707","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":"100707"}}