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.

翻译：目的：开发一种基于深度学习的系统，用于在正位胸部X光片上识别亚临床动脉粥样硬化。方法与结果：基于460例一级预防患者（58.4%男性，中位年龄63[51-74]岁）的胸部X光片（80%训练队列、20%内部验证队列）开发了预测冠状动脉钙化（CAC）评分（即AI-CAC模型）的深度学习算法。所有患者均因临床指征在3个月内完成配对胸部X光片和胸部计算机断层扫描（CT）。以胸部CT计算的CAC评分作为金标准。该模型在同一机构的90例时间独立队列中进行了验证（外部验证）。主要结局为AI-CAC模型通过曲线下面积（AUC）评估的诊断准确性。总体而言，AI-CAC评分中位数为35（0-388），28.9%患者无AI-CAC。在内部验证队列和外部验证队列中，AI-CAC模型识别CAC>0的AUC分别为0.90和0.77。两个队列的敏感度均持续高于92%。在总体队列（n=540）中，AI-CAC=0的患者在4.3年后仅发生1次动脉粥样硬化性心血管疾病（ASCVD）事件。AI-CAC>0患者的Kaplan-Meier ASCVD事件估计值显著更高（13.5% vs. 3.4%，对数秩检验=0.013）。结论：AI-CAC模型似乎能以高敏感度在胸部X光片上准确检测亚临床动脉粥样硬化，并以高阴性预测值预测ASCVD事件。将AI-CAC模型应用于心血管风险分层优化或作为机会性筛查工具需通过前瞻性研究进行评估。