通过基于图像的深度学习在胸部X射线上检测亚临床动脉样硬化
Guglielmo Gallone1,2, Francesco Iodice3, Alberto Presta3
1Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza Hospital, Corso Bramante 88/90, 10126, Turin, Italy.
一个新的AI模型可以在胸部X射线上检测亚临床动脉样硬化,预测心血管事件. 这种深度学习工具显示了冠状动脉 (CAC) 评分的高灵敏度和负预测值.
科学领域:
- 医疗成像中的人工智能
- 心血管疾病预测预测
- 放射学和诊断成像 放射学和诊断成像
背景情况:
- 亚临床动脉样硬化是心血管事件的前体.
- 早期发现动脉样硬化有助于风险分层和预防.
- 胸部X射线广泛可用,但在心血管风险评估中未得到充分利用.
研究的目的:
- 开发一种深度学习系统,以在普通前额胸部X射线上识别亚临床动脉样硬化.
- 创建一个AI算法 (AI-CAC模型) 来预测冠状动脉 (CAC) 评分.
- 评估模型在识别冠状动脉化和预测动脉样性心血管疾病 (ASCVD) 事件中的准确性.
主要方法:
- 在460张胸部X射线上训练了一种深度学习算法,并配对CT扫描以获得CAC得分.
- AI-CAC模型在内部对90名患者进行了验证,并在外部对单独的队列进行了验证.
- 使用曲线下的面积 (AUC) 评估诊断准确性,CT的CAC得分作为基准真相.
主要成果:
- 在AI-CAC模型中,在内部验证中获得了0.90的AUC,在外部验证中获得了0.77的AUC,用于识别CAC>0.
- 在两个验证队列中,检测CAC的灵敏度始终在92%以上.
- 与AI-CAC=0患者相比,AI-CAC>0患者的ASCVD事件发生率显著更高.
结论:
- AI-CAC模型在胸部X射线上检测亚临床动脉样硬化时表现出高灵敏度.
- 该模型显示了预测高负预测值的ASCVD事件的潜力.
- 建议进行前性评估,以完善心血管风险分层和机会性查.
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