Deep learning versus radiologists for acute aortic dissection on CT: A systematic review and network meta-analysis.
Authors
Affiliations (9)
Affiliations (9)
- Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Biophotonics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., Taipei 112304, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Institute of Biophotonics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., Taipei 112304, Taiwan.
- Division of Medical Imaging, Health Management Center, Cheng Hsin General Hospital, Taipei, Taiwan, ROC.
- Institute of Biophotonics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., Taipei 112304, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Emergency Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of Cardiovascular Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Heart Center, Cheng Hsin General Hospital, Taipei, Taiwan, ROC.
- Institute of Biophotonics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist., Taipei 112304, Taiwan; National Yang Ming Chiao Tung University, Brain Research Center, Taipei, Taiwan; National Yang Ming Chiao Tung University, College Medical Device Innovation and Translation Center, Taipei, Taiwan. Electronic address: [email protected].
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.