{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:27:50Z","timestamp":1787063270085,"version":"build-2736575974"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a deep learning system to differentiate demyelinating optic neuritis (ON) and non-arteritic anterior ischemic optic neuropathy (NAION) with overlapping clinical profiles at the acute phase.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a deep learning system (ONION) to distinguish ON from NAION at the acute phase. Color fundus photographs (CFPs) from 871 eyes of 547 patients were included, including 396 ON from 232 patients and 475 NAION from 315 patients. Efficientnet-B0 was used to train the model, and the performance was measured by calculating the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Also, Cohen\u2019s kappa coefficients were obtained to compare the system\u2019s performance to that of different ophthalmologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the validation data set, the ONION system distinguished between acute ON and NAION achieved the following mean performance: time-consuming (23 s), AUC 0.903 (95% CI 0.827\u20130.947), sensitivity 0.796 (95% CI 0.704\u20130.864), and specificity 0.865 (95% CI 0.783\u20130.920). Testing data set: time-consuming (17 s), AUC 0.902 (95% CI 0.832\u20130.944), sensitivity 0.814 (95% CI 0.732\u20130.875), and specificity 0.841 (95% CI 0.762\u20130.897). The performance (\u03ba = 0.805) was comparable to that of a retinal expert (\u03ba = 0.749) and was better than the other four ophthalmologists (\u03ba = 0.309\u20130.609).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The ONION system performed satisfactorily distinguishing ON from NAION at the acute phase. It might greatly benefit the challenging differentiation between ON and NAION.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2023.1188542","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T06:29:36Z","timestamp":1688020176000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fundus 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