Role of Artificial Intelligence in Detecting and Classifying Aortic Dissection: Where Are We? A Systematic Review and Meta-Analysis.
Journal:
Radiology. Cardiothoracic imaging
Published Date:
Jun 1, 2025
Abstract
Purpose To evaluate the diagnostic performance of artificial intelligence (AI) models in detecting and classifying aortic dissection (AD) from CT images through a systematic review and meta-analysis. Materials and Methods PubMed, Web of Science, Embase, and Medline were searched for articles published from January 2010 to October 2023. All primary studies were included. Quality of evidence was assessed using a composite tool based on the METhodological RadiomICs Score (ie, METRICS) and Checklist for Artificial Intelligence in Medical Imaging (ie, CLAIM) checklists, and risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (ie, QUADAS-2) tool. Univariate and bivariate meta-analyses were performed assessing individual and joint estimates of sensitivity and specificity. Results Thirteen studies were identified, with most using contrast-enhanced CT (CECT) imaging ( = 9) and the remainder using noncontrast CT (NCCT) imaging as their model input. Only three studies presented algorithms classifying AD by Stanford criteria. Univariate analysis of AI detection performance estimated sensitivity at 94% (95% CI: 88, 97; = .049) and specificity at 88% (95% CI: 79, 94; < .001). Bivariate analysis showed good overall model performances (area under the receiver operating characteristic curve [AUC], 0.97 [95% CI: 0.95, 0.99]; = .49). Subgroup analyses revealed good performance for models using CECT images (sensitivity, 97% [95% CI: 81, 100; = .007]; specificity, 93% [95% CI: 87, 97; < .001]; AUC, 0.98 [95% CI: 0.93, 0.99; = .09]) and NCCT images (sensitivity, 91% [95% CI: 83, 96; = .33); specificity, 84% [95% CI: 69, 93; < .001); AUC, 0.95 [95% CI: 0.90, 0.99; = .14]). Most studies were of low quality and had high risk of bias. Conclusion AI can feasibly detect AD but does not demonstrate clinical applicability in its current form. CT, Vascular, Cardiac, Aorta, Computer-aided Diagnosis (CAD), Meta-Analysis © RSNA, 2025.
