{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T17:17:33Z","timestamp":1787764653333,"version":"build-2784847793"},"reference-count":17,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"8","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>To analyze the efficacy of a deep learning (DL)-based artificial intelligence (AI)-based algorithm in detecting the presence of diabetic retinopathy (DR) and glaucoma suspect as compared to the diagnosis by specialists secondarily to explore whether the use of this algorithm can reduce the cross-referral in three clinical settings: a diabetologist clinic, retina clinic, and glaucoma clinic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>This is a prospective observational study. Patients between 35 and 65 years of age were recruited from glaucoma and retina clinics at a tertiary eye care hospital and a physician\u2019s clinic. Non-mydriatic fundus photography was performed according to the disease-specific protocols. These images were graded by the AI system and specialist graders and comparatively analyzed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Out of 1085 patients, 362 were seen at glaucoma clinics, 341 were seen at retina clinics, and 382 were seen at physician clinics. The kappa agreement between AI and the glaucoma grader was 85% [95% confidence interval (CI): 77.55\u201392.45%], and retina grading had 91.90% (95% CI: 87.78\u201396.02%). The retina grader from the glaucoma clinic had 85% agreement, and the glaucoma grader from the retina clinic had 73% agreement. The sensitivity and specificity of AI glaucoma grading were 79.37% (95% CI: 67.30\u201388.53%) and 99.45 (95% CI: 98.03\u201399.93), respectively; DR grading had 83.33% (95 CI: 51.59\u201397.91) and 98.86 (95% CI: 97.35\u201399.63). The cross-referral accuracy of DR and glaucoma was 89.57% and 95.43%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>DL-based AI systems showed high sensitivity and specificity in both patients with DR and glaucoma; also, there was a good agreement between the specialist graders and the AI system.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/ijo.ijo_11_23","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T03:33:05Z","timestamp":1690860785000},"page":"3039-3045","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":11,"title":["Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma"],"prefix":"10.4103","volume":"71","author":[{"given":"Janani","family":"Surya","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya,Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Garima","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya,Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neha","family":"Pandy","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya,Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tyler","family":"Hyungtaek Rim","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore"},{"name":"Department of Science and Technology, Medi Whale, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geunyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Science and Technology, Medi Whale, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"MN Swathi","family":"Priya","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya,Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brughanya","family":"Subramanian","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya,Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajiv","family":"Raman","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya,Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"R1-20240828","doi-asserted-by":"crossref","first-page":"678","DOI":"10.4103\/ijo.IJO_667_20","article-title":"Diabetic retinopathy screening guidelines in India:All India Ophthalmological Society diabetic retinopathy task force and Vitreoretinal Society of India Consensus Statement","volume":"69","author":"Raman","year":"2021","journal-title":"Indian J Ophthalmol"},{"key":"R2-20240828","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1038\/s41433-019-0626-5","article-title":"The impact of artificial intelligence in screening for diabetic retinopathy in India","volume":"34","author":"Rajalakshmi","year":"2020","journal-title":"Eye (Lond)"},{"key":"R3-20240828","doi-asserted-by":"crossref","first-page":"2993","DOI":"10.4103\/ijo.IJO_1420_21","article-title":"Using artificial intelligence for diabetic retinopathy screening:Policy implications","volume":"69","author":"Raman","year":"2021","journal-title":"Indian J Ophthalmol"},{"key":"R4-20240828","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.2337\/dc20-1877","article-title":"Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems","volume":"44","author":"Lee","year":"2021","journal-title":"Diabetes Care"},{"key":"R5-20240828","doi-asserted-by":"crossref","first-page":"16993","DOI":"10.1038\/s41598-021-96438-w","article-title":"Distribution of diabetic retinopathy in diabetes mellitus patients and its association rules with other eye diseases","volume":"11","author":"Yao","year":"2021","journal-title":"Sci Rep"},{"issue":"(Suppl 9)","key":"R6-20240828","first-page":"228","article-title":"International clinical diabetic retinopathy disease severity scale. 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