{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:50:48Z","timestamp":1791946248157,"version":"4.3.4"},"reference-count":47,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Stroke"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:sec>\n            <jats:title>BACKGROUND:<\/jats:title>\n            <jats:p>Moyamoya disease (MMD) is a rare and complex pathological condition characterized by an abnormal collateral circulation network in the basal brain. The diagnosis of MMD and its progression is unpredictable and influenced by many factors. MMD can affect the blood vessels supplying the eyes, resulting in a range of ocular symptoms. In this study, we developed a deep learning model using real-world data to assist a diagnosis and determine the stage of the disease using retinal photographs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>METHODS:<\/jats:title>\n            <jats:p>This retrospective observational study conducted from August 2006 to March 2022 included 498 retinal photographs from 78 patients with MMD and 3835 photographs from 1649 healthy participants. Photographs were preprocessed, and an ResNeXt50 model was developed. Model performance was measured using receiver operating curves and their area under the receiver operating characteristic curve, accuracy, sensitivity, and F1-score. Heatmaps and progressive erasing plus progressive restoration were performed to validate the faithfulness.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS:<\/jats:title>\n            <jats:p>Overall, 322 retinal photographs from 67 patients with MMD and 3752 retinal photographs from 1616 healthy participants were used to develop a screening and stage prediction model for MMD. The average age of the patients with MMD was 44.1 years, and the average follow-up time was 115 months. Stage 3 photographs were the most prevalent, followed by stages 4, 5, 2, 1, and 6 and healthy. The MMD screening model had an average area under the receiver operating characteristic curve of 94.6%, with 89.8% sensitivity and 90.4% specificity at the best cutoff point. MMD stage prediction models had an area under the receiver operating characteristic curve of 78% or higher, with stage 3 performing the best at 93.6%. Heatmap identified the vascular region of the fundus as important for prediction, and progressive erasing plus progressive restoration result shows an area under the receiver operating characteristic curve of 70% only with 50% of the important regions.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>CONCLUSIONS:<\/jats:title>\n            <jats:p>This study demonstrated that retinal photographs could be used as potential biomarkers for screening and staging of MMD and the disease stage could be classified by a deep learning algorithm.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/strokeaha.123.044026","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T10:00:46Z","timestamp":1706004046000},"page":"715-724","update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":21,"title":["Screening of Moyamoya Disease From Retinal Photographs: Development and Validation of Deep Learning Algorithms"],"prefix":"10.1161","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3228-6843","authenticated-orcid":true,"given":"JaeSeong","family":"Hong","sequence":"first","affiliation":[{"name":"Department of Biomedical Systems Informatics (J.H., Y.R.P.), Yonsei University College of Medicine, Seoul, Republic of Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0454-9597","authenticated-orcid":true,"given":"Sangchul","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Medical Humanities and Social Sciences (S.Y.), Yonsei University College of Medicine, Seoul, Republic of Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9441-7354","authenticated-orcid":true,"given":"Kyu Won","family":"Shim","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery (K.W.S.), Yonsei University College of Medicine, Seoul, Republic of Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4210-2094","authenticated-orcid":true,"given":"Yu Rang","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Biomedical Systems Informatics (J.H., Y.R.P.), Yonsei University College of Medicine, Seoul, Republic of Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1001\/archneur.1969.00480090076012"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.2176\/jns-nmc.2022-0072"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.2176\/nmc.52.245"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(08)70240-0"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1055\/s-2008-1052540"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.STR.0000182256.32489.99"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.119.025609"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1097\/RCT.0000000000000834"},{"key":"e_1_3_3_10_2","unstructured":"Lee AG Al Othman B Prospero Ponce C Kini A. 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