{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:33:41Z","timestamp":1787312021849,"version":"build-2736575974"},"reference-count":39,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000061","name":"Fogarty International Center","doi-asserted-by":"crossref","award":["TW011404"],"award-info":[{"award-number":["TW011404"]}],"id":[{"id":"10.13039\/100000061","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Technology &amp; Therapeutics"],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>To identify retinal microvascular features that distinguish normoglycemia from diabetes mellitus (DM)[Study-1] and prediabetes (PreDM)[Study-2] among Asian Indians using artificial intelligence (AI); evaluate their diagnostic accuracy; and examine independent associations of oculomics scores [Oculomic Diabetes Score (ODiS) and Prediabetes Score (OPreS)] with glycemic status.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We analyzed 273 retinal images from 139 participants (19 = normoglycemia, 100 = DM, 20 = PreDM; mean age: 49.4\u2009\u00b1\u200912.9 yrs; male: 71.2%) with dataset randomly split (50:50) into training and test sets. We extracted 226 quantitative vessel tortuosity features separately for arteries and veins using machine vision-based approaches. Top six discriminating features were separately selected (using Wilcoxon Rank-Sum test and Linear Discriminant Analysis) on training sets and validated on independent blinded test sets. Model performances were evaluated using area under precision-recall curve (AUPRC) for DM and area under the receiver operating characteristic curve (AUC) for PreDM. Independent association of ODiS and OPreS (adjusting for age, sex, height, weight, blood pressure, serum cholesterol, serum creatinine) was assessed by multivariable logistic regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Specific oculomics-based retinal vascular features distinguished DM\/PreDM from normoglycemia. Vein-only model achieved AUPRC = 0.96 (95% confidence interval [95% CI]: 0.9\u20131.00) (sensitivity = 95%, specificity = 72.2%, precision = 95%) on S\n                      <jats:sub>v1<\/jats:sub>\n                      for DM and an AUC = 0.80 (95% CI: 0.63\u20130.94) (sensitivity = 70%, specificity = 80%, precision = 82.35%) on S\n                      <jats:sub>v2<\/jats:sub>\n                      for PreDM. Oculomics scores were independently associated with DM(ODiS) [Adjusted Odds ratio (AdjOR): 2.00 (95% CI: 1.56\u20132.57,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.001)] but not with PreDM(OPreS) [AdjOR: 1.32 (95% CI: 0.65\u20132.71,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.48)].\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>In this proof-of-concept study, AI-informed retinal venous features on routine fundus images, with further prospective and multisite validation, could potentially serve as noninvasive DM detection using AI models among Asian Indians.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15209156261417296","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T10:49:16Z","timestamp":1769165356000},"page":"782-791","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Diagnostic Accuracy of Artificial Intelligence-Enabled Retinal Biomarkers for Detecting Type 2 Diabetes and Prediabetes Among Asian Indians [DART Study]"],"prefix":"10.1177","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5908-5921","authenticated-orcid":false,"given":"Soujanya","family":"Kaup","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Yenepoya (Deemed to be) University, Mangalore, India."},{"name":"Faculty of Epidemiology and Population Health, London School of Hygiene &amp; Tropical Medicine, London, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudeshna","family":"Sil Kar","sequence":"additional","affiliation":[{"name":"Wallace H. Coulter Department of Biomedical Engineering, Emory University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodrigo M.","family":"Carrillo-Larco","sequence":"additional","affiliation":[{"name":"Emory Global Diabetes Research Center, Woodruff Health Sciences Center, and Emory University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohan","family":"Dhamdhere","sequence":"additional","affiliation":[{"name":"Wallace H. Coulter Department of Biomedical Engineering, Emory University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1149-2525","authenticated-orcid":false,"given":"Mendel","family":"Lebowitz","sequence":"additional","affiliation":[{"name":"Wallace H. Coulter Department of Biomedical Engineering, Emory University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ranjit Mohan","family":"Anjana","sequence":"additional","affiliation":[{"name":"Dr. Mohan\u2019s Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), Chennai, India."},{"name":"Department of Diabetology, Madras Diabetes Research Foundation (ICMR Collaborating Centre of Excellence), Chennai, India."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram","family":"Jagannathan","sequence":"additional","affiliation":[{"name":"Emory Global Diabetes Research Center, Woodruff Health Sciences Center, and Emory University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed K.","family":"Ali","sequence":"additional","affiliation":[{"name":"Emory Global Diabetes Research Center, Woodruff Health Sciences Center, and Emory University, Atlanta, Georgia, USA."},{"name":"Department of Family and Preventive Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viswanathan","family":"Mohan","sequence":"additional","affiliation":[{"name":"Dr. Mohan\u2019s Diabetes Specialities Centre (IDF Centre of Excellence in Diabetes Care), Chennai, India."},{"name":"Department of Diabetology, Madras Diabetes Research Foundation (ICMR Collaborating Centre of Excellence), Chennai, India."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.M. Venkat","family":"Narayan","sequence":"additional","affiliation":[{"name":"Emory Global Diabetes Research Center, Woodruff Health Sciences Center, and Emory University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anant","family":"Madabhushi","sequence":"additional","affiliation":[{"name":"Wallace H. 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