{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T14:14:24Z","timestamp":1788358464844,"version":"build-2803163510"},"reference-count":45,"publisher":"Association for Research in Vision and Ophthalmology (ARVO)","issue":"9","license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"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":[[2026,9,2]]},"DOI":"10.1167\/tvst.15.9.3","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T13:30:44Z","timestamp":1788355844000},"page":"3","source":"Crossref","is-referenced-by-count":0,"title":["Improving Generalization of Deep Learning for Glaucoma Classification Under Real-World Domain Shift via Unsupervised Domain Adaptation"],"prefix":"10.1167","volume":"15","author":[{"given":"Homa","family":"Rashidisabet","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, USA"},{"name":"Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA"},{"name":"Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. V. Paul","family":"Chan","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA"},{"name":"Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thasarat Sutabutr","family":"Vajaranant","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA"},{"name":"Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darvin","family":"Yi","sequence":"additional","affiliation":[{"name":"Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA"},{"name":"Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27","reference":[{"issue":"11","key":"bib2","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","article-title":"Global prevalence of glaucoma and projections of glaucoma burden through 2040 a systematic review and meta-analysis","volume":"121","author":"Tham","year":"2020","journal-title":"Ophthalmology"},{"issue":"17","key":"bib3","doi-asserted-by":"publisher","first-page":"3523","DOI":"10.1038\/s41433-023-02547-3","article-title":"Which OCT parameters can best predict visual field progression in glaucoma?","volume":"37","author":"Sethi","year":"2023","journal-title":"Eye"},{"issue":"6","key":"bib4","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1007\/s00401-016-1609-2","article-title":"Glaucoma: the retina and beyond","volume":"132","author":"Davis","year":"2016","journal-title":"Acta Neuropathol"},{"issue":"1","key":"bib5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.mjafi.2015.12.001","article-title":"Glaucoma: the \u2018black hole\u2019 of irreversible blindness","volume":"72","author":"Parihar","year":"2016","journal-title":"Med J Armed Forces India"},{"issue":"4","key":"bib6","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.ogla.2020.04.012","article-title":"Predicting glaucoma before onset using deep learning","volume":"3","author":"Thakur","year":"2020","journal-title":"Ophthalmol Glaucoma"},{"issue":"3","key":"bib7","doi-asserted-by":"publisher","first-page":"100180","DOI":"10.1016\/j.xops.2022.100180","article-title":"Early glaucoma detection by using style transfer to predict retinal nerve fiber layer thickness distribution on the fundus photograph","volume":"2","author":"Chen","year":"2022","journal-title":"Ophthalmol Sci"},{"issue":"1","key":"bib8","doi-asserted-by":"publisher","first-page":"100233","DOI":"10.1016\/j.xops.2022.100233","article-title":"Detecting glaucoma from fundus photographs using deep learning without convolutions: transformer for improved generalization","volume":"3","author":"Fan","year":"2023","journal-title":"Ophthalmol Sci"},{"key":"bib11","doi-asserted-by":"publisher","first-page":"e16","DOI":"10.1016\/j.ogla.2022.02.010","article-title":"Artificial intelligence for glaucoma: creating and implementing artificial intelligence for disease detection and progression","volume":"5","author":"Al-Aswad","year":"2022","journal-title":"Ophthalmol Glaucoma"},{"issue":"12","key":"bib12","doi-asserted-by":"publisher","first-page":"AIoa2300221","DOI":"10.1056\/AIoa2300221","article-title":"Development and validation of a multimodal multitask vision foundation model for generalist ophthalmic artificial intelligence","volume":"1","author":"Qiu","year":"2024","journal-title":"NEJM AI"},{"issue":"3","key":"bib13","doi-asserted-by":"publisher","first-page":"100703","DOI":"10.1016\/j.xops.2025.100703","article-title":"Utilization of image-based deep learning in multimodal glaucoma detection neural network from a primary patient cohort","volume":"5","author":"Hwang","year":"2025","journal-title":"Ophthalmol Sci"},{"issue":"3","key":"bib14","doi-asserted-by":"publisher","first-page":"100300","DOI":"10.1016\/j.xops.2023.100300","article-title":"Characteristics of a large, labeled data set for the training of artificial intelligence for glaucoma screening with fundus photographs","volume":"3","author":"Lemij","year":"2023","journal-title":"Ophthalmol Sci"},{"issue":"1","key":"bib16","doi-asserted-by":"publisher","first-page":"AIcs2400497","DOI":"10.1056\/AIcs2400497","article-title":"How generalizable are foundation models when applied to different demographic groups and settings?","volume":"2","author":"Xiong","year":"2025","journal-title":"NEJM AI"},{"issue":"12","key":"bib17","doi-asserted-by":"publisher","first-page":"AIe2401024","DOI":"10.1056\/AIe2401024","article-title":"A new foundation model for multimodal ophthalmic images: advancing disease detection and prediction","volume":"1","author":"Chia","year":"2024","journal-title":"NEJM AI"},{"issue":"6","key":"bib18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1167\/tvst.14.6.3","article-title":"Robust uncertainty-informed glaucoma classification under data shift","volume":"14","author":"Rashidisabet","year":"2025","journal-title":"Transl Vis Sci Technol"},{"issue":"6","key":"bib19","doi-asserted-by":"publisher","first-page":"e210097","DOI":"10.1148\/ryai.2021210097","article-title":"Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification","volume":"3","author":"Eche","year":"2021","journal-title":"Radiol Artif Intell"},{"issue":"1","key":"bib20","doi-asserted-by":"publisher","first-page":"100623","DOI":"10.1016\/j.xops.2024.100623","article-title":"Evaluating a foundation artificial intelligence model for glaucoma detection using color fundus photographs","volume":"5","author":"Chuter","year":"2025","journal-title":"Ophthalmol Sci"},{"issue":"1","key":"bib21","doi-asserted-by":"publisher","first-page":"100233","DOI":"10.1016\/j.xops.2022.100233","article-title":"Detecting glaucoma from fundus photographs using deep learning without convolutions: transformer for improved generalization","volume":"3","author":"Fan","year":"2023","journal-title":"Ophthalmol Sci"},{"key":"bib22","doi-asserted-by":"publisher","first-page":"106061","DOI":"10.1016\/j.bspc.2024.106061","article-title":"Domain adaptation-based deep learning model for forecasting and diagnosis of glaucoma disease","volume":"92","author":"Madadi","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"bib23","doi-asserted-by":"publisher","first-page":"119863","DOI":"10.1016\/j.neuroimage.2023.119863","article-title":"DomainATM: domain adaptation toolbox for medical data analysis","volume":"268","author":"Guan","year":"2023","journal-title":"Neuroimage"},{"key":"bib24","first-page":"7541","article-title":"mixDA: mixup domain adaptation for glaucoma detection on fundus images","volume":"3","author":"Yan","year":"2023","journal-title":"Neural Comput Appl"},{"issue":"1","key":"bib25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/116.00000192","article-title":"Deep unsupervised domain adaptation: a review of recent advances and perspectives","volume":"11","author":"Liu","year":"2022","journal-title":"APSIPA Trans Signal Inf Process"},{"issue":"1","key":"bib26","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/JBHI.2021.3085770","article-title":"Unsupervised domain adaptation based image synthesis and feature alignment for joint optic disc and cup segmentation","volume":"26","author":"Lei","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"key":"bib27","doi-asserted-by":"publisher","first-page":"106530","DOI":"10.1016\/j.cmpb.2021.106530","article-title":"ECSD-Net: a joint optic disc and cup segmentation and glaucoma classification network based on unsupervised domain adaptation","volume":"213","author":"Liu","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"issue":"10","key":"bib28","doi-asserted-by":"publisher","first-page":"5792","DOI":"10.1109\/JBHI.2023.3342422","article-title":"Unsupervised domain adaptation fundus image segmentation via multi-scale adaptive adversarial learning","volume":"28","author":"Zhou","year":"2024","journal-title":"IEEE J Biomed Health Inform"},{"issue":"22","key":"bib31","doi-asserted-by":"publisher","first-page":"8748","DOI":"10.3390\/s22228748","article-title":"Unsupervised domain adaptation with shape constraint and triple attention for joint optic disc and cup segmentation","volume":"22","author":"Zhang","year":"2022","journal-title":"Sensors"},{"key":"bib32","doi-asserted-by":"publisher","first-page":"108759","DOI":"10.1016\/j.compbiomed.2024.108759","article-title":"Unsupervised domain adaptation multi-level adversarial learning-based crossing-domain retinal vessel segmentation","volume":"178","author":"Liu","year":"2024","journal-title":"Comput Biol Med"},{"issue":"7","key":"bib33","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1007\/s11548-020-02144-9","article-title":"WGAN domain adaptation for the joint optic disc-and-cup segmentation in fundus images","volume":"15","author":"Kadambi","year":"2020","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"2","key":"bib35","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.ophtha.2022.09.014","article-title":"Training deep learning models to work on multiple devices by cross-domain learning with no additional annotations","volume":"130","author":"Wu","year":"2023","journal-title":"Ophthalmology"},{"issue":"8","key":"bib36","doi-asserted-by":"publisher","first-page":"5374","DOI":"10.1002\/mp.17012","article-title":"Two-stage adversarial learning based unsupervised domain adaptation for retinal OCT segmentation","volume":"51","author":"Diao","year":"2024","journal-title":"Med Phys"},{"key":"bib38","doi-asserted-by":"publisher","first-page":"101570","DOI":"10.1016\/j.media.2019.101570","article-title":"REFUGE challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs","volume":"59","author":"Orlando","year":"2020","journal-title":"Med Image Anal"},{"issue":"3","key":"bib39","doi-asserted-by":"publisher","first-page":"161","DOI":"10.5566\/ias.2346","article-title":"Rim-One Dl: a unified retinal image database for assessing glaucoma using deep learning","volume":"39","author":"Fumero","year":"2020","journal-title":"Image Anal Stereol"},{"issue":"11","key":"bib40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1167\/tvst.12.11.8","article-title":"Validating the generalizability of ophthalmic artificial intelligence models on real-world clinical data","volume":"12","author":"Rashidisabet","year":"2023","journal-title":"Transl Vis Sci Technol"},{"issue":"11","key":"bib42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1167\/tvst.12.11.8","article-title":"Validating the generalizability of ophthalmic artificial intelligence models on real-world clinical data","volume":"12","author":"Rashidisabet","year":"2023","journal-title":"Transl Vis Sci Technol"},{"key":"bib1","first-page":"349","article-title":"Epidemiological variations and trends in health burden of glaucoma worldwide","author":"Wang","year":"2019"},{"key":"bib9","doi-asserted-by":"crossref","DOI":"10.1109\/IRI.2019.00037","article-title":"Deep multi-task learning for interpretable glaucoma detection","volume-title":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","author":"Mojab"},{"key":"bib10","first-page":"10563","article-title":"Attention based glaucoma detection: a large-scale database and CNN model","volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","author":"Li","year":"2019"},{"key":"bib15","article-title":"Towards neural networks that provably know when they don't know","author":"Meinke","year":"2020"},{"key":"bib29","doi-asserted-by":"crossref","DOI":"10.1109\/IJCNN48605.2020.9207358","article-title":"A GAN-based domain adaptation method for glaucoma diagnosis","volume-title":"Proceedings of the International Joint Conference on Neural Networks","author":"Sun","year":"2020"},{"key":"bib30","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-43205-7","volume-title":"GANs for Data Augmentation in Healthcare","author":"Solanki","year":"2023"},{"key":"bib34","doi-asserted-by":"crossref","DOI":"10.1109\/ISBI.2018.8363637","article-title":"Domain adaptation for biomedical image segmentation using adversarial training","volume-title":"IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","author":"Javanmardi","year":"2018"},{"key":"bib37","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proceedings of the 32nd International Conference on Machine Learning","author":"Ganin","year":"2015"},{"key":"bib41","doi-asserted-by":"crossref","DOI":"10.5220\/0010311405660574","article-title":"I-ODA, real-world multi-modal longitudinal data for ophthalmic applications","author":"Mojab","year":"2021"},{"key":"bib43","first-page":"770","article-title":"Deep residual learning for image recognition","volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","author":"He","year":"2016"},{"key":"bib44","first-page":"839","article-title":"Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks","volume-title":"Proceedings\u20142018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018. Vol 2018-January","author":"Chattopadhay","year":"2018"},{"key":"bib45","doi-asserted-by":"crossref","DOI":"10.1109\/ISBIC56247.2022.9854763","article-title":"Deep Dirichlet uncertainty for unsupervised out-of-distribution detection of eye fundus photographs in glaucoma screening","volume-title":"ISBIC 2022\u2014International Symposium on Biomedical Imaging Challenges, Proceedings","author":"Araojo","year":"2022"}],"container-title":["Translational Vision Science & Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/tvst.arvojournals.org\/article.aspx?doi=10.1167\/tvst.15.9.3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T13:30:48Z","timestamp":1788355848000},"score":1,"resource":{"primary":{"URL":"http:\/\/tvst.arvojournals.org\/article.aspx?doi=10.1167\/tvst.15.9.3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,2]]},"references-count":45,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,9,1]]}},"URL":"https:\/\/doi.org\/10.1167\/tvst.15.9.3","relation":{},"ISSN":["2164-2591"],"issn-type":[{"value":"2164-2591","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9,2]]}}}