{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T14:26:21Z","timestamp":1786112781935,"version":"build-2736575974"},"reference-count":0,"publisher":"Radiological Society of North America (RSNA)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Radiology: Artificial Intelligence"],"abstract":"<jats:sec>\n                    <jats:title\/>\n                    <jats:p>Purpose To evaluate anatomy-specific diagnostic performance of CT angiography-based artificial intelligence (AI) for anterior circulation occlusion detection and negative-result implications for distal occlusions. Materials and Methods In this Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies (PRISMA-DTA)-compliant, prospectively registered systematic review and diagnostic meta-analysis, PubMed, Embase, and Web of Science were searched for AI-based CT angiography studies from January 1, 2019, through March 11, 2026. Pooled sensitivity and specificity were estimated for global large-vessel occlusions (LVOs), combined internal carotid artery\/first segment of the middle cerebral artery (ICA\/M1) occlusions, and distal second\/third segments of the middle cerebral artery (M2\/M3) occlusions with a bivariate random-effects model. Evidence certainty was assessed with GRADE; a supportive record-level multilevel bivariate generalized linear mixed model (GLMM) adjusted for occlusion territory, study design, algorithm type, publication year, and section thickness. Results Thirty-one reports involving 15,708 patients were included. Pooled sensitivity\/specificity were 80.1%\/91.9% for global LVOs, 91.6%\/92.7% for ICA\/M1 occlusions, and 52.7%\/94.8% for M2\/M3 occlusions. In the supportive adjusted model, ICA\/M1 occlusions had higher sensitivity than global LVOs (OR, 2.28; P &lt; .001), whereas M2\/M3 occlusions had lower sensitivity (OR, 0.25; P &lt; .001). Evidence certainty was moderate for ICA\/M1 sensitivity and very low for M2\/M3 sensitivity. For M2\/M3 occlusions, a negative AI result yielded an LR\u2212 of 0.50 (95% CI, 0.28\u20130.73), corresponding to posttest probabilities of 17.6% and 33.3% at 30% and 50% pretest probabilities. Conclusion CT angiography-based AI performance varied by occlusion territory; lower M2\/M3 sensitivity limited negative-result reliability. \u00a9RSNA, 2026<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1148\/ryai.260261","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T13:57:06Z","timestamp":1785333426000},"source":"Crossref","is-referenced-by-count":0,"title":["Anatomy-specific Performance of CT Angiography\u2013based AI for                     Anterior Circulation Occlusion: Systematic Review and                     Meta-Analysis"],"prefix":"10.1148","author":[{"given":"Yike","family":"Liu","sequence":"first","affiliation":[{"name":"Dongsheng District People\u2019s Hospital, Ordos,                     China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7113-7825","authenticated-orcid":false,"given":"Chaofan","family":"Li","sequence":"additional","affiliation":[{"name":"Medical College of Qinghai University, Xingning,                     China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zepeng","family":"Ren","sequence":"additional","affiliation":[{"name":"Ordos Central Hospital, Ordos Clinical Medical College,                     Inner Mongolia Medical University, Ordos, China"},{"name":"Department of Gastroenterology, Inner Mongolia Medical                     University Affiliated Inner Mongolia Autonomous Region People\u2019s Hospital,                     Hohhot, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4540-3076","authenticated-orcid":false,"given":"Yuchen","family":"Hu","sequence":"additional","affiliation":[{"name":"Inner Mongolia Medical University, Hohhot, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0356-802X","authenticated-orcid":false,"given":"Chaohua","family":"Wang","sequence":"additional","affiliation":[{"name":"Medical College of Qinghai University, Xingning,                     China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyan","family":"Chen","sequence":"additional","affiliation":[{"name":"Bayannur City Hospital, Inner Mongolia Medical                     University, Bayannur, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7451-081X","authenticated-orcid":false,"given":"Xijin","family":"Liu","sequence":"additional","affiliation":[{"name":"Ordos Central Hospital, Street Address, Ordos, Inner                     Mongolia, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1577","published-online":{"date-parts":[[2026,7,29]]},"container-title":["Radiology: Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/pubs.rsna.org\/doi\/pdf\/10.1148\/ryai.260261","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T16:13:33Z","timestamp":1785946413000},"score":1,"resource":{"primary":{"URL":"http:\/\/pubs.rsna.org\/doi\/10.1148\/ryai.260261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,29]]},"references-count":0,"alternative-id":["10.1148\/ryai.260261"],"URL":"https:\/\/doi.org\/10.1148\/ryai.260261","relation":{},"ISSN":["2638-6100"],"issn-type":[{"value":"2638-6100","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,29]]},"article-number":"e260261"}}