{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T16:10:49Z","timestamp":1792858249055,"version":"4.5.0"},"reference-count":41,"publisher":"Association for Research in Vision and Ophthalmology (ARVO)","issue":"9","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"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":[[2025,9,11]]},"DOI":"10.1167\/tvst.14.9.16","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T14:00:48Z","timestamp":1757599248000},"page":"16","source":"Crossref","is-referenced-by-count":2,"title":["Code-Free Machine Learning for the Detection of Common Ophthalmic Diseases"],"prefix":"10.1167","volume":"14","author":[{"given":"Trevor","family":"Lin","sequence":"first","affiliation":[{"name":"Morsani College of Medicine, University of South Florida, Tampa, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Theodore","family":"Leng","sequence":"additional","affiliation":[{"name":"Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27","reference":[{"issue":"2","key":"bib1","doi-asserted-by":"publisher","first-page":"e144","DOI":"10.1016\/S2214-109X(20)30489-7","article-title":"Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study","volume":"9","author":"Steinmetz","year":"2021","journal-title":"Lancet Global Health"},{"issue":"1","key":"bib2","doi-asserted-by":"publisher","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 Digit Med"},{"key":"bib3","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"},{"key":"bib4","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.ophtha.2017.10.031","article-title":"Fully automated detection and quantification of macular fluid in OCT using deep learning","volume":"125","author":"Schlegl","year":"2018","journal-title":"Ophthalmology"},{"key":"bib5","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s11517-018-1915-z","article-title":"The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment","volume":"57","author":"Yoo","year":"2019","journal-title":"Med Biol Eng Comput"},{"key":"bib6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-019-0649-y","article-title":"CNNs for automatic glaucoma assessment using fundus images: an extensive validation","volume":"18","author":"Diaz-Pinto","year":"2019","journal-title":"Biomed Eng Online"},{"key":"bib7","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1038\/s41746-024-01389-x","article-title":"Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease","volume":"7","author":"Abr\u00e0moff","year":"2024","journal-title":"NPJ Digit Med"},{"issue":"7","key":"bib8","doi-asserted-by":"publisher","first-page":"101095","DOI":"10.1016\/j.xcrm.2023.101095","article-title":"Artificial intelligence in ophthalmology: The path to the real-world clinic","volume":"4","author":"Li","year":"2023","journal-title":"Cell Rep Med"},{"key":"bib9","doi-asserted-by":"publisher","first-page":"106398","DOI":"10.1016\/j.cmpb.2021.106398","article-title":"Code-free cloud computing service to facilitate rapid biomedical digital signal processing and algorithm development","volume":"211","author":"Jennings","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"bib10","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1016\/j.bushor.2023.04.003","article-title":"Democratizing artificial intelligence: How no-code AI can leverage machine learning operations","volume":"66","author":"Sundberg","year":"2023","journal-title":"Business Horizons"},{"issue":"4","key":"bib11","doi-asserted-by":"publisher","first-page":"100495","DOI":"10.1016\/j.xops.2024.100495","article-title":"A user-friendly approach for the diagnosis of diabetic retinopathy using ChatGPT and automated machine learning","volume":"4","author":"Mohammadi","year":"2024","journal-title":"Ophthalmol Sci"},{"issue":"5","key":"bib12","doi-asserted-by":"publisher","first-page":"e232","DOI":"10.1016\/S2589-7500(19)30108-6","article-title":"Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study","volume":"1","author":"Faes","year":"2019","journal-title":"Lancet Digit Health"},{"issue":"1","key":"bib13","doi-asserted-by":"publisher","first-page":"19528","DOI":"10.1038\/s41598-020-76665-3","article-title":"Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience","volume":"10","author":"Antaki","year":"2020","journal-title":"Sci Rep"},{"key":"bib14","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1016\/j.oret.2023.03.003","article-title":"Performance of automated machine learning for diabetic retinopathy image classification from multi-field handheld retinal images","volume":"7","author":"Jacoba","year":"2023","journal-title":"Ophthalmol Retina"},{"key":"bib15","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1038\/s42256-021-00305-2","article-title":"Code-free deep learning for multi-modality medical image classification","volume":"3","author":"Korot","year":"2021","journal-title":"Nat Mach Intell"},{"key":"bib16","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1136\/bjophthalmol-2020-316108","article-title":"Classification of pachychoroid disease on ultrawide-field indocyanine green angiography using auto-machine learning platform","volume":"105","author":"Kim","year":"2021","journal-title":"Br J Ophthalmol"},{"key":"bib17","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1001\/jamaophthalmol.2023.6318","article-title":"Automated machine learning for predicting diabetic retinopathy progression from ultra-widefield retinal images","volume":"142","author":"Silva","year":"2024","journal-title":"JAMA Ophthalmol"},{"key":"bib18","first-page":"2461","article-title":"Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration","volume":"260","author":"Abbas","year":"2022","journal-title":"Graefes Arch Clin Exp Ophthalmol"},{"key":"bib19","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.oret.2020.06.016","article-title":"Diabetic retinopathy screening with automated retinal image analysis in a primary care setting improves adherence to ophthalmic care","volume":"5","author":"Liu","year":"2021","journal-title":"Ophthalmol Retina"},{"key":"bib20","doi-asserted-by":"publisher","first-page":"94","DOI":"10.4103\/ijo.IJO_657_17","article-title":"The diagnostic accuracy of single-and five-field fundus photography in diabetic retinopathy screening by primary care physicians","volume":"66","author":"Srihatrai","year":"2018","journal-title":"Ind J Ophthalmol"},{"key":"bib21","doi-asserted-by":"crossref","DOI":"10.1071\/HC24040","article-title":"Use of a smartphone-based, non-mydriatic fundus camera for patients with red flag ophthalmic presentations in a rural general practice","author":"Davidson","year":"2024","journal-title":"J Primary Health Care"},{"issue":"10","key":"bib22","doi-asserted-by":"publisher","first-page":"e114","DOI":"10.1016\/j.ophtha.2022.07.012","article-title":"Improving access to eye care: a systematic review of the literature","volume":"129","author":"Solomon","year":"2022","journal-title":"Ophthalmology"},{"key":"bib23","doi-asserted-by":"crossref","first-page":"m689","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"Nagendran","year":"2020","journal-title":"BMJ"},{"key":"bib24","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"issue":"1","key":"bib25","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1038\/s41597-022-01564-3","article-title":"Fives: a fundus image dataset for artificial Intelligence based vessel segmentation","volume":"9","author":"Jin","year":"2022","journal-title":"Sci Data"},{"key":"bib26","doi-asserted-by":"publisher","first-page":"117151","DOI":"10.1016\/j.image.2024.117151","article-title":"Benchmarking deep models on retinal fundus disease diagnosis and a large-scale dataset","volume":"127","author":"Xia","year":"2024","journal-title":"Signal Proc Image Commun"},{"key":"bib29","article-title":"A deep learning approach for the screening of referable age-related macular degeneration\u2013Model development and external validation","author":"Hsu","journal-title":"J Formosan Med Assoc"},{"key":"bib30","doi-asserted-by":"publisher","first-page":"107302","DOI":"10.1016\/j.cmpb.2022.107302","article-title":"Binary and multi-class automated detection of age-related macular degeneration using convolutional-and transformer-based architectures","volume":"229","author":"Dominguez","year":"2023","journal-title":"Comput Methods Programs Biomed"},{"key":"bib31","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.3390\/diagnostics13101738","article-title":"Automatic diagnosis of glaucoma from retinal images using deep learning approach","volume":"13","author":"Shoukat","year":"2023","journal-title":"Diagnostics"},{"key":"bib32","doi-asserted-by":"publisher","first-page":"2702","DOI":"10.4103\/ijo.IJO_92_21","article-title":"Identification of glaucoma from fundus images using deep learning techniques","volume":"69","author":"Ajitha","year":"2021","journal-title":"Ind J Ophthalmol"},{"issue":"1","key":"bib33","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3390\/jimaging10010008","article-title":"A robust machine learning model for diabetic retinopathy classification","volume":"10","author":"Tabacaru","year":"2023","journal-title":"J Imaging"},{"issue":"1","key":"bib36","doi-asserted-by":"publisher","first-page":"10395","DOI":"10.1038\/s41598-024-60807-y","article-title":"Automated machine learning model for fundus image classification by health-care professionals with no coding experience","volume":"14","author":"Zago Ribeiro","year":"2024","journal-title":"Sci Rep"},{"issue":"4","key":"bib37","doi-asserted-by":"publisher","first-page":"100721","DOI":"10.1016\/j.xops.2025.100721","article-title":"Code-free deep learning glaucoma detection on color fundus images","volume":"5","author":"Milad","year":"2025","journal-title":"Ophthalmol Sci"},{"issue":"1","key":"bib38","doi-asserted-by":"publisher","first-page":"2437294","DOI":"10.1080\/28338073.2024.2437294","article-title":"Revolutionizing diabetic retinopathy screening: integrating AI-based retinal imaging in primary care","volume":"14","author":"Beals","year":"2025","journal-title":"J CME"},{"key":"bib39","first-page":"1","article-title":"Feasibility and Patient Experience of a Pilot Artificial Intelligence-Based Diabetic Retinopathy Screening Program in Northern Ontario","author":"Bhambhwani","year":"2024","journal-title":"Ophthalmic Epidemiology"},{"key":"bib40","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.4103\/IJO.IJO_2151_23","article-title":"Evaluation of an AI algorithm trained on an ethnically diverse dataset to screen a previously unseen population for diabetic retinopathy","volume":"72","author":"Rao","year":"2024","journal-title":"Ind J Ophthalmol"},{"issue":"2","key":"bib41","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1167\/tvst.10.2.13","article-title":"Addressing artificial intelligence bias in retinal diagnostics","volume":"10","author":"Burlina","year":"2021","journal-title":"Transl Vis Sci Technol"},{"key":"bib27","author":"Cloud Data Processing Addendum (Customers)"},{"key":"bib28","author":"Hyperparameter tuning in Cloud Machine Learning Engine using Bayesian Optimization. Blog"},{"key":"bib34","first-page":"1258","volume-title":"Classification of ocular diseases employing attention-based unilateral and bilateral feature weighting and fusion","author":"He","year":"2020"},{"key":"bib35","first-page":"1","volume-title":"Dense correlation network for automated multi-label ocular disease detection with paired color fundus photographs","author":"Li","year":"2020"}],"container-title":["Translational Vision Science & Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/tvst.arvojournals.org\/article.aspx?articleid=2810813","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T14:00:50Z","timestamp":1757599250000},"score":1,"resource":{"primary":{"URL":"https:\/\/tvst.arvojournals.org\/article.aspx?articleid=2810813"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"references-count":41,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9,2]]}},"URL":"https:\/\/doi.org\/10.1167\/tvst.14.9.16","relation":{},"ISSN":["2164-2591"],"issn-type":[{"value":"2164-2591","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9,11]]}}}