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 an temporally-independent 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 in the internal validation
cohort and 0.77 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 elevated
sensitivity, and to predict ASCVD events with elevated negative predictive
value. Adoption of the AI-CAC model to refine CV risk stratification or as an
opportunistic screening tool requires prospective evaluation.

通过基于深度学习的系统在普通胸部 X 线上识别亚临床动脉硬化，开发了一个预测冠状动脉钙化分数的深度学习算法，在内、外部验证中表现出良好的诊断准确性和预测动脉粥样硬化心血管疾病事件的负预测价值。