{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T13:25:07Z","timestamp":1790342707704,"version":"4.1.0"},"reference-count":39,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"6","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"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":[[2024,6]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning\u2013based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1\u2013M3, and M4\u2013M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models\u2019 performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5\u00b112.4, 67.6\u00b113.8, and 67.9\u00b113.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850\u20130.925), with a sensitivity of 80.1% (95% CI, 72.0\u201388.1) and a specificity of 88.6% (95% CI, 84.7\u201392.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/strokeaha.123.045772","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T14:00:46Z","timestamp":1716559246000},"page":"1609-1618","update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":22,"title":["Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography"],"prefix":"10.1161","volume":"55","author":[{"given":"Pyeong Eun","family":"Kim","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Center, JLK Inc, Seoul, Republic of Korea (P.E.K., H.Y., D.K., W.-S.R.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyojung","family":"Yang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center, JLK Inc, Seoul, Republic of Korea (P.E.K., H.Y., D.K., W.-S.R.)."},{"name":"Department of Computer Science and Technology, University of Cambridge, United Kingdom (H.Y.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4121-5035","authenticated-orcid":true,"given":"Dongmin","family":"Kim","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center, JLK Inc, Seoul, Republic of Korea (P.E.K., H.Y., D.K., W.-S.R.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0374-8658","authenticated-orcid":true,"given":"Leonard","family":"Sunwoo","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University College of Medicine, Republic of Korea (L.S.)."},{"name":"Department of Radiology (L.S.), Seoul National University Bundang Hospital, Seongnam, Republic of Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0423-7297","authenticated-orcid":true,"given":"Chi Kyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Neurology, Korea University Guro Hospital, Seoul, Republic of Korea (C.K.K.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2719-3012","authenticated-orcid":true,"given":"Beom Joon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Neurology (B.J.K.), Seoul National University Bundang Hospital, Seongnam, Republic of Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4028-8339","authenticated-orcid":true,"given":"Joon-Tae","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Neurology, Chonnam National University Hospital, Gwangju, Republic of Korea (J.-T.K.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2823-5253","authenticated-orcid":true,"given":"Wi-Sun","family":"Ryu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center, JLK Inc, Seoul, Republic of Korea (P.E.K., H.Y., D.K., W.-S.R.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2269-8644","authenticated-orcid":true,"given":"Ho Sung","family":"Kim","sequence":"additional","affiliation":[{"name":"USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles (H.S.K.)."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.5853\/jos.2023.00752"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.5853\/jos.2022.03286"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.120.030326"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2022.807145"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2019190142"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-85143-3"},{"key":"e_1_3_3_8_2","first-page":"1534","article-title":"Use of the Alberta Stroke Program Early CT Score (ASPECTS) for assessing CT scans in patients with acute stroke.","volume":"22","author":"Pexman JW","year":"2001","unstructured":"Pexman, JW, Barber, PA, Hill, MD, Sevick, RJ, Demchuk, AM, Hudon, ME, Hu, WY, Buchan, AM. 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