Deep learning versus radiologists for acute aortic dissection on CT: A systematic review and network meta-analysis.

Abstract

Acute aortic dissection (AAD) is a time-sensitive cardiovascular emergency in which delayed recognition remains associated with poor early outcomes, and deep learning (DL) applied to computed tomography (CT) is increasingly proposed for triage support. We synthesized DL detection accuracy on CT, compared DL with radiologists head-to-head, and secondarily pooled CT segmentation accuracy. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant review searched PubMed, Embase, and Web of Science through February 18, 2026. Detection studies were pooled with bivariate random-effects models; head-to-head comparisons were synthesized with contrast-based network meta-analysis; segmentation Sørensen-Dice coefficients were analyzed with three-level random-effects models. Twenty-three studies were included. For non-contrast CT detection (10 studies; 18 cohorts; 54 algorithm-cohort datasets), pooled sensitivity was 0.91 (95 % confidence interval [CI], 0.87-0.94) and specificity 0.90 (95 % CI, 0.85-0.94). In direct comparisons (5 studies; 11 comparisons), radiologist-to-DL relative sensitivity was 0.72 (95 % CI, 0.57-0.91) and relative specificity 0.88 (95 % CI, 0.83-0.93). For the secondary segmentation synthesis (16 studies; 88 datasets), pooled Dice was 0.863 (95 % CI, 0.795-0.931), lower for true- and false-lumen than for whole-aorta delineation. DL showed encouraging detection accuracy and higher sensitivity and specificity than the radiologist comparators in five head-to-head studies read under experimental conditions, supporting assistive triage rather than replacement; lumen-level segmentation remains more challenging than whole-aorta contouring. Certainty was low - datasets were predominantly case-control with a median prevalence of 50 %, heterogeneity was substantial and small-study effects pronounced - so these estimates are experimental upper bounds requiring prospective confirmation.