Anatomy-specific Performance of CT Angiography-based AI for Anterior Circulation Occlusion: Systematic Review and Meta-Analysis.

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 < .001), whereas M2/M3 occlusions had lower sensitivity (OR, 0.25; P < .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- of 0.50 (95% CI, 0.28-0.73), 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. ©RSNA, 2026.