Commercial AI Model Diagnostic Accuracy for Intracranial Large- and Medium-Vessel Occlusion at Emergency CT Angiography.

Abstract

The diagnostic accuracy of AIDOC-VO, the first commercial artificial intelligence (AI) tool for intracranial large- and medium-vessel occlusion (LVO and MeVO) detection at head and neck CT angiography (CTA), was evaluated in a multicenter emergency setting. A prospective diagnostic-accuracy study of 3031 adult CTA examinations (mean age ± SD, 67.3 years ± 16.4; 1549 females) acquired March-July 2024 across a 10-hospital region was performed. The AI model was compared with clinical radiology reporting. Examinations flagged positive or doubt by either the AI model or report underwent blinded rereading for reference-standard establishment. Of 3031 CTA examinations, valid AI model output was yielded for 2804 (92.5%), of which 224 of 2804 (8.0%) had vessel occlusion (VO) on reference-standard reading. For VO detection within intended use (218 of 224), sensitivity was 81.7% (178 of 218) (clinical report: 81.2% [177 of 218]; P = .91), and specificity was 99.6% (2569 of 2580) (clinical report: 99.3% [2561 of 2580]; P = .12). LVO sensitivity was 92.8% (64 of 69) (clinical report: 87.0% [60 of 69]; P = .42) and MeVO sensitivity was 76.1% (121 of 159) (clinical report: 79.2% [126 of 159]; P = .55). The AI model identified VOs missed by radiologists in 42 examinations, for an enhanced detection rate of 18.8% (42 of 224; 15 per 1000 CT angiograms), and generated 11 false alerts (3.9 per 1000 CT angiograms). Performance did not differ significantly from clinical radiology reporting. Keywords: CT, CT-Angiography, CNS, Ischemia/Infarction, Stroke, Diagnosis, Classification, Application Domain, Arteries, Artificial Intelligence, Large Vessel Occlusion, Medium Vessel Occlusion Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.