Abstract Objectives To develop an automated deep learning framework for detecting aortic dissection (AD) and visualizing its morphology and extent on non-contrast CT (NCCT) images. Materials and methods This retrospective study included patients who underwent aortic CTA from January 2021 to January 2023 at two tertiary hospitals. Demographic data, medical history, and CT scans were collected. A segmentation-based deep learning model was trained to identify true and false lumens on NCCT images, with performance evaluated on internal and external test sets. Segmentation accuracy was measured using the Dice coefficient, while the intraclass correlation coefficient (ICC) assessed consistency between predicted and ground-truth false lumen volumes. Receiver operating characteristic (ROC) analysis evaluated the model’s predictive performance. Results Among 701 patients (median age, 53 years, IQR: 41–64, 486 males), data from Center 1 were split into training (439 cases: 318 non-AD, 121 AD) and internal test sets (106 cases: 77 non-AD, 29 AD) (8:2 ratio), while Center 2 served as the external test set (156 cases: 80 non-AD, 76 AD). The ICC for false lumen volume was 0.823 (95% CI: 0.750–0.880) internally and 0.823 (95% CI: 0.760–0.870) externally. The model achieved an AUC of 0.935 (95% CI: 0.894–0.968) in the external test set, with an optimal cutoff of 7649 mm 3 yielding 88.2% sensitivity, 91.3% specificity, and 89.0% negative predictive value. Conclusions The proposed deep learning framework accurately detects AD on NCCT and effectively visualizes its morphological features, demonstrating strong clinical potential. Critical relevance statement This deep learning framework helps reduce the misdiagnosis of AD in emergencies with limited time. The satisfactory results of presenting true/false lumen on NCCT images benefit patients with contrast media contraindications and promote treatment decisions. Key Points False lumen volume was used as an indicator for AD. NCCT detects AD via this segmentation model. This framework enhances AD diagnosis in emergencies, reducing unnecessary contrast use. Graphical Abstract
Wang et al. (Thu,) studied this question.