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

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 ( = 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.