Detection of subclinical atherosclerosis by image-based deep learning on chest X-ray

Abstract

Aims To develop a deep-learning-based system for recognition of subclinical atherosclerosis on a plain frontal chest X-ray. Methods and results A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest X-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients [58.4% male, median age 63 (51–74) years] with available paired chest X-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on a temporally independent validation cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by the area under the curve (AUC) was the primary outcome. Overall, median AI-CAC score was 35 (0–388) and 28.9% patients had no AI-CAC. AUC of the AI-CAC model to identify a CAC >0 was 0.90 (95%CI 0.84–0.97) in the internal validation cohort and 0.77 (95%CI 0.67–0.86) in the external validation cohort. Sensitivity was consistently above 92% in both cohorts. In the overall cohort (n = 540), among patients with AI-CAC = 0, a single ASCVD event occurred, after 4.3 years. Patients with AI-CAC > 0 had significantly higher Kaplan Meier estimates for ASCVD events (13.5% vs. 3.4%, log-rank = 0.013). Conclusion The AI-CAC model seems to accurately detect subclinical atherosclerosis on chest X-ray with high sensitivity, and to predict ASCVD events with high negative predictive value. Adoption of the AI-CAC model to refine CV risk stratification or as an opportunistic screening tool requires prospective evaluation. Lay summary The AI-CAC is the first deep-learning-based model to accurately detect subclinical atherosclerosis on chest X-ray with high sensitivity, and to predict atherosclerotic cardiovascular disease events with high negative predictive value. Patients with AI-CAC > 0 had a significantly higher risk for atherosclerotic cardiovascular disease events. Adjusted for the European Society of Cardiology cardiovascular risk grading, AI-CAC retained borderline significance with 5-year atherosclerotic cardiovascular disease events. The AI-CAC might be used as a cheap, widely available modality to identify individuals with subclinical atherosclerosis benefiting from lipid-lowering therapies, or as an opportunistic screening tool. © The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology.