{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T13:24:23Z","timestamp":1785936263484,"version":"3.56.0"},"reference-count":37,"publisher":"Association for Research in Vision and Ophthalmology (ARVO)","issue":"7","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Trans. Vis. Sci. Tech."],"published-print":{"date-parts":[[2025,7,1]]},"DOI":"10.1167\/tvst.14.7.1","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T14:46:22Z","timestamp":1751381182000},"page":"1","source":"Crossref","is-referenced-by-count":7,"title":["Equitable Deep Learning for Diabetic Retinopathy Detection Using Multidimensional Retinal Imaging With Fair Adaptive Scaling"],"prefix":"10.1167","volume":"14","author":[{"given":"Min","family":"Shi","sequence":"first","affiliation":[{"name":"Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Muneeb","family":"Afzal","sequence":"additional","affiliation":[{"name":"Tandon School of Engineering, New York University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Huang","sequence":"additional","affiliation":[{"name":"Tandon School of Engineering, New York University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congcong","family":"Wen","sequence":"additional","affiliation":[{"name":"Tandon School of Engineering, New York University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Luo","sequence":"additional","affiliation":[{"name":"Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Osama","family":"Khan","sequence":"additional","affiliation":[{"name":"Tandon School of Engineering, New York University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Tian","sequence":"additional","affiliation":[{"name":"Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo","family":"Kim","sequence":"additional","affiliation":[{"name":"Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Fang","sequence":"additional","affiliation":[{"name":"Tandon School of Engineering, New York University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27","reference":[{"issue":"suppl_1","key":"bib1","doi-asserted-by":"publisher","first-page":"s84","DOI":"10.2337\/diacare.27.2007.S84","article-title":"Retinopathy in Diabetes","volume":"27","author":"Fong","year":"2004","journal-title":"Diabetes Care"},{"key":"bib2","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1001\/jama.298.8.902","article-title":"Management of diabetic retinopathy: a systematic review","volume":"298","author":"Mohamed","year":"2007","journal-title":"JAMA"},{"key":"bib3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40662-015-0026-2","article-title":"Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss","volume":"2","author":"Lee","year":"2015","journal-title":"Eye Vis"},{"key":"bib4","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1001\/archopht.122.4.552","article-title":"The prevalence of diabetic retinopathy among adults in the United States","volume":"122","author":"Kempen","year":"2004","journal-title":"Arch Ophthalmol"},{"key":"bib5","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1001\/jama.2010.1111","article-title":"Prevalence of diabetic retinopathy in the United States, 2005\u20132008","volume":"304","author":"Zhang","year":"2010","journal-title":"JAMA"},{"key":"bib6","doi-asserted-by":"publisher","first-page":"748","DOI":"10.2337\/diacare.16.5.748","article-title":"Racial differences in the relationship between blood pressure and risk of retinopathy among individuals with NIDDM","volume":"16","author":"Harris","year":"1993","journal-title":"Diabetes Care"},{"key":"bib7","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.ajo.2005.08.063","article-title":"Diabetic retinopathy in a multi-ethnic cohort in the United States","volume":"141","author":"Wong","year":"2006","journal-title":"Am J Ophthalmol"},{"key":"bib8","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.2337\/diacare.21.8.1230","article-title":"Is the risk of diabetic retinopathy greater in non-Hispanic Blacks and Mexican Americans than in Non-Hispanic Whites with type 2 diabetes?: A U.S. population study","volume":"21","author":"Harris","year":"1998","journal-title":"Diabetes Care"},{"key":"bib9","doi-asserted-by":"publisher","first-page":"779","DOI":"10.2337\/diacare.22.5.779","article-title":"Black-white differences in risk of developing retinopathy among individuals with type 2 diabetes","volume":"22","author":"Harris","year":"1999","journal-title":"Diabetes Care"},{"key":"bib10","doi-asserted-by":"publisher","first-page":"034","DOI":"10.17352\/ijcem.000027","article-title":"Diabetic retinopathy: focus on minority populations","volume":"3","author":"Barsegian","year":"2017","journal-title":"Int J Clin Endocrinol Metab"},{"key":"bib11","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1001\/archopht.126.10.1421","article-title":"Diabetes mellitus and visual impairment","volume":"126","author":"Zhang","year":"2008","journal-title":"Arch Ophthalmol"},{"key":"bib12","doi-asserted-by":"publisher","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":"bib13","doi-asserted-by":"publisher","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":"bib14","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated identification of diabetic retinopathy using deep learning","volume":"124","author":"Gargeya","year":"2017","journal-title":"Ophthalmology"},{"issue":"1","key":"bib15","doi-asserted-by":"publisher","first-page":"3242","DOI":"10.1038\/s41467-021-23458-5","article-title":"A deep learning system for detecting diabetic retinopathy across the disease spectrum","volume":"12","author":"Dai","year":"2021","journal-title":"Nat Commun"},{"issue":"1","key":"bib16","doi-asserted-by":"publisher","first-page":"e10","DOI":"10.1016\/S2589-7500(20)30250-8","article-title":"Predicting the risk of developing diabetic retinopathy using deep learning","volume":"3","author":"Bora","year":"2021","journal-title":"Lancet Digit Health"},{"issue":"1","key":"bib17","doi-asserted-by":"publisher","first-page":"e35","DOI":"10.1016\/S2589-7500(19)30004-4","article-title":"Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study","volume":"1","author":"Bellemo","year":"2019","journal-title":"Lancet Digit Health"},{"key":"bib21","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1001\/jamaophthalmol.2023.1310","article-title":"Association of biomarker-based artificial intelligence with risk of racial bias in retinal images","volume":"141","author":"Coyner","year":"2023","journal-title":"JAMA Ophthalmol"},{"issue":"8","key":"bib22","doi-asserted-by":"publisher","first-page":"e25165","DOI":"10.2196\/25165","article-title":"Gender prediction for a multiethnic population via deep learning across different retinal fundus photograph fields: retrospective cross-sectional study","volume":"9","author":"Betzler","year":"2021","journal-title":"JMIR Med Inform"},{"key":"bib25","doi-asserted-by":"publisher","first-page":"3009","DOI":"10.1109\/JBHI.2021.3049452","article-title":"Reinventing 2D convolutions for 3D images","volume":"25","author":"Yang","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"bib32","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/s41746-023-00805-y","article-title":"An adversarial training framework for mitigating algorithmic biases in clinical machine learning","volume":"6","author":"Yang","year":"2023","journal-title":"NPJ Digit Med"},{"key":"bib35","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.ajo.2018.10.007","article-title":"Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images","volume":"198","author":"Asaoka","year":"2019","journal-title":"Am J Ophthalmol"},{"key":"bib36","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1109\/TBME.2020.3027231","article-title":"DcardNet: diabetic retinopathy classification at multiple levels based on structural and angiographic optical coherence tomography","volume":"68","author":"Zang","year":"2021","journal-title":"IEEE Trans Biomed Eng"},{"key":"bib18","first-page":"12289","article-title":"FairCLIP: Harnessing Fairness in Vision-Language Learning","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Luo","year":"2024"},{"key":"bib19","doi-asserted-by":"crossref","article-title":"FairDomain: achieving fairness in cross-domain medical image segmentation and classification","author":"Tian","DOI":"10.1007\/978-3-031-73116-7_15"},{"key":"bib20","article-title":"FairSeg: a large-scale medical image segmentation dataset for fairness learning using segment anything model with fair error-bound scaling","author":"Tian"},{"key":"bib23","first-page":"6105","article-title":"EfficientNet: rethinking model scaling for convolutional neural networks","volume-title":"International Conference on Machine Learning","author":"Tan","year":"2019"},{"key":"bib24","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy"},{"key":"bib26","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan"},{"key":"bib27","doi-asserted-by":"crossref","first-page":"9992","DOI":"10.1109\/ICCV48922.2021.00986","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","volume-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Liu","year":"2021"},{"key":"bib28","first-page":"770","article-title":"Deep residual learning for image recognition","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"He","year":"2016"},{"key":"bib29","doi-asserted-by":"crossref","first-page":"11966","DOI":"10.1109\/CVPR52688.2022.01167","article-title":"A ConvNet for the 2020s","volume-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Liu","year":"2022"},{"key":"bib30","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1109\/CVPR.2017.243","article-title":"Densely Connected Convolutional Networks","volume-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Huang","year":"2017"},{"key":"bib31","first-page":"11492","article-title":"To be robust or to be fair: towards fairness in adversarial training","volume-title":"International Conference on Machine Learning","author":"Xu","year":"2021"},{"key":"bib33","doi-asserted-by":"crossref","first-page":"20311","DOI":"10.1109\/CVPR52729.2023.01945","article-title":"Bias Mimicking: A Simple Sampling Approach for Bias Mitigation","volume-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Qraitem","year":"2023"},{"key":"bib34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIPTEKNO.2019.8894965","article-title":"Transfer learning for early and advanced glaucoma detection with convolutional neural networks","volume-title":"2019 Medical Technologies Congress (TIPTEKNO)","author":"Serener","year":"2019"},{"key":"bib37","article-title":"Harvard Eye Fairness: A Large-Scale 3D Imaging Dataset for Equitable Eye Diseases Screening and Fair Identity Scaling","author":"Luo"}],"container-title":["Translational Vision Science & Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/tvst.arvojournals.org\/article.aspx?articleid=2803200","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T14:46:27Z","timestamp":1751381187000},"score":1,"resource":{"primary":{"URL":"https:\/\/tvst.arvojournals.org\/article.aspx?articleid=2803200"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":37,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7,1]]}},"URL":"https:\/\/doi.org\/10.1167\/tvst.14.7.1","relation":{},"ISSN":["2164-2591"],"issn-type":[{"value":"2164-2591","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7,1]]}}}