{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T06:22:50Z","timestamp":1787638970719,"version":"build-2736575974"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Diabetic retinopathy (DR) remains the leading cause of blindness among working-age adults and requires regular screening to detect progression among the growing global diabetic population. This study evaluated the performance of AEYE-DS, an autonomous artificial intelligence (AI) system designed for high-throughput, point-of-care analysis of retinal images, in detecting more-than-mild diabetic retinopathy (mtmDR) during routine screening of patients with diabetes who had not previously been diagnosed with DR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Principal results<\/jats:title>\n                    <jats:p>AEYE-DS was tested across three prospective clinical studies using two FDA-cleared non-mydriatic retinal cameras: the handheld Aurora and the desktop Topcon NW400. The algorithm autonomously analyzed retinal images and determined mtmDR presence. Diagnostic outcomes were compared to a reference standard based on the Early Treatment for Diabetic Retinopathy Study (ETDRS) severity grading performed by multi-expert review at an independent reading center. Sensitivity and specificity were 93% and 91% in AEYE-1 (95% CI: 83%\u201397% and 88%\u201394%), 92% and 94% in AEYE-2 (95% CI: 79%\u201397% and 90%\u201396%), and 93% and 89% in AEYE-3 (95% CI: 80%\u201397% and 85%\u201392%). Imageability was &amp;gt;99% in all studies. Intra-operator repeatability exceeded 99% for both devices. Between-operator reproducibility was 98% for the desktop camera and 95% for the handheld device, while between-device reproducibility reached 99% and 97%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AEYE-DS demonstrated high diagnostic accuracy, imageability, reliability, and reproducibility across different operators and devices in non-mydriatic settings. Findings support autonomous AI system use for scalable, point-of-care DR screening, potentially expanding access, streamlining workflows, and reducing the global burden of diabetic eye disease.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1907804","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T05:34:20Z","timestamp":1787636060000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Autonomous AI-driven point-of-care screening for diabetic retinopathy compared to reading center multi-expert clinical review: results from three prospective controlled pivotal validation studies with AEYE-DS in over 1,200 patients"],"prefix":"10.3389","volume":"8","author":[{"given":"Zack","family":"Dvey-Aharon","sequence":"first","affiliation":[{"name":"AEYE Health Inc.","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moshe","family":"Livne","sequence":"additional","affiliation":[{"name":"AEYE Health Inc.","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Margalit","sequence":"additional","affiliation":[{"name":"AEYE Health Inc.","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit","family":"Wohl","sequence":"additional","affiliation":[{"name":"AEYE Health Inc.","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adi","family":"Haupt","sequence":"additional","affiliation":[{"name":"AEYE Health Inc.","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachelle","family":"Aviv","sequence":"additional","affiliation":[{"name":"AEYE Health Inc.","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahava","family":"Stein","sequence":"additional","affiliation":[{"name":"A. Stein \u2013 Regulatory Affairs Consulting Ltd.","place":["Kfar Saba, Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adi","family":"Rachelson","sequence":"additional","affiliation":[{"name":"A. 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