{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T03:33:17Z","timestamp":1789702397074,"version":"4.0.1"},"reference-count":44,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"13","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:sec id=\"jah311056-sec-0001\" xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Prompt diagnosis of acute central retinal artery occlusion (CRAO) is crucial for therapeutic management and stroke prevention. However, most stroke centers lack onsite ophthalmic expertise before considering fibrinolytic treatment. This study aimed to develop, train, and test a deep learning system to detect hyperacute CRAO on retinal fundus photographs within the critical 4.5\u2010hour treatment window and up to 24\u2009hours after visual loss to aid in secondary stroke prevention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"jah311056-sec-0002\" xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">Our retrospective, cross\u2010sectional study included 1322 color fundus photographs from 771 patients with acute visual loss due to CRAO, central retinal vein occlusion, nonarteritic anterior ischemic optic neuropathy, and healthy controls. Photographs were collected from 9 expert neuro\u2010ophthalmology centers in 6 countries, including 3 randomized clinical trials. Training included 1039 photographs (517 patients), followed by testing on 2 data sets: (1) hyperacute CRAO (54 photographs, 54 patients) and (2) CRAO within 24\u2009hours after visual loss (110 photographs, 109 patients).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"jah311056-sec-0003\" xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">The deep learning system achieved an area under the receiver operating characteristic curve of 0.96 (95% confidence interval (CI), 0.95\u20130.98), a sensitivity of 92.6% (95% CI, 87.0\u201398.0), and a specificity of 85.0% (95% CI, 81.8\u201392.8) for detecting CRAO at hyperacute stage, with similar results within 24\u2009hours. The deep learning system outperformed stroke neurologists on a subset of hyperacute testing data set (120 photographs, 120 patients).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"jah311056-sec-0004\" xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">A deep learning system can accurately detect hyperacute CRAO on retinal photographs within a time window compatible with urgent fibrinolysis. If further validated, such systems could improve patient selection for fibrinolytic trials and optimize secondary stroke prevention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"jah311056-sec-0005\" xml:lang=\"en\">\n                    <jats:title>Registration<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      URL:\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.clinicaltrials.gov\">https:\/\/www.clinicaltrials.gov<\/jats:ext-link>\n                      ; Unique identifier: NCT06390579.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.124.041441","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T05:58:17Z","timestamp":1751003897000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":15,"title":["Artificial 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