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深度学习通过胸部X射线图来估计心血管风险:风险预测研究

Jakob Weiss¹, Vineet K Raghu², Kaavya Paruchuri³

¹Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, and Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts, and Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany (J.W.).

Annals of internal medicine

|March 25, 2024

概括

一个新的深度学习模型,CXR CVD-Risk,使用胸部X射线来预测10年心血管风险,在数据缺失的情况下表现优于传统得分. 这种人工智能工具有助于识别高风险个体,以进行动脉样硬化心血管疾病 (ASCVD) 的初级预防.

科学领域:

人工智能在医学中的应用
预防心血管疾病预防心血管疾病
医学成像分析医学成像分析

背景情况:

目前关于动脉样硬化心血管疾病 (ASCVD) 初级预防的指南依赖风险计算器,但基本的输入数据往往无法获得.
这种限制需要对机会性心血管风险评估的补充方法.

研究的目的:

开发和验证一种深度学习模型,CXR CVD-风险,用于估计使用胸部X射线图 (CXR) 估计10年内发生重大心血管不良事件 (MACE) 的风险.
为了比较CXR CVD风险与传统的ASCVD风险评分的性能,特别是对于他类药物的资格确定.

主要方法:

该CXR心血管疾病风险模型是使用癌症查试验数据开发的.
外部验证对8,869名由于缺少数据而存在未知的ASCVD风险的门诊患者和2,132名已知风险的门诊患者进行.
这项研究比较了CXR CVD风险和ASCVD风险评分的10年MACE预测.

主要成果:

在未知ASCVD风险的患者中,CXR CVD-Risk识别出具有7.5%或更高10年MACE风险 (调整HR,1.73) 的患者.
在已知ASCVD风险的患者中,CXR CVD风险提供了超出传统ASCVD得分 (调整HR,1.88) 的风险预测.

结论:

基于单个胸部X射线的CXR CVD-Risk可以预测超出当前临床标准的10年MACE.
该模型在识别高心血管风险的个体方面表现有希望,由于数据不完整,可能无法通过传统方法进行评估.

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