{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T16:51:55Z","timestamp":1790182315728,"version":"4.1.0"},"reference-count":23,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2017R1C1B5017929"],"award-info":[{"award-number":["2017R1C1B5017929"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist\u2019s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.<\/jats:p>","DOI":"10.3390\/jpm11050321","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T12:47:07Z","timestamp":1619009227000},"page":"321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases"],"prefix":"10.3390","volume":"11","author":[{"given":"Kyoung Min","family":"Kim","sequence":"first","affiliation":[{"name":"Data Scientist Team, BEGAS Inc., Sejong-daero 39, Jung-gu, Seoul 04513, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0505-5559","authenticated-orcid":false,"given":"Tae-Young","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Information and Statistics, Chungbuk National University, Chungdae-ro 1, Seowon-gu, Cheongju-si, Chungbuk 28644, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aesul","family":"Kim","sequence":"additional","affiliation":[{"name":"Laboratory of Pharmacology, College of Pharmacy and Medical Research Center, Chungbuk National University, 194-31 Osongsaengmyeong 1-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungbuk 28160, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joohee","family":"Kim","sequence":"additional","affiliation":[{"name":"Laboratory of Pharmacology, College of Pharmacy and Medical Research Center, Chungbuk National University, 194-31 Osongsaengmyeong 1-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungbuk 28160, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyu Jin","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Ulsan University Hospital, College of Medicine, University of Ulsan, 877, Bangeojinsunhwando-ro, Dong-gu, Ulsan 44033, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaesuk","family":"Yun","sequence":"additional","affiliation":[{"name":"Laboratory of Pharmacology, College of Pharmacy and Medical Research Center, Chungbuk National University, 194-31 Osongsaengmyeong 1-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungbuk 28160, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8006-8560","authenticated-orcid":false,"given":"Jung Kee","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Ulsan University Hospital, College of Medicine, University of Ulsan, 877, Bangeojinsunhwando-ro, Dong-gu, Ulsan 44033, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"ref_1","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":"Abramoff","year":"2018","journal-title":"NPJ Digit. 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