人工智能增强心电图在心血管诊断和风险预测中的表型选择性
medRxiv : the preprint server for health sciences
|August 20, 2025
概括
人工智能 (AI) 增强的心电图 (ECG) 模型显示了广泛的心血管关联,而不仅仅是特定的条件. 这些AI-ECG工具比精确的诊断分类器更好地作为一般心血管风险标志物发挥作用.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 生物标志物发现发现
背景情况:
- 人工智能 (AI) 增强的心电图 (ECG) 模型被开发用于识别特定的心脏异常.
- 了解AI-ECG模型的表型关联的选择性对于其临床应用至关重要.
- 目前的研究调查了AI-ECG模型是否作为特定条件的分类器或更广泛的心血管风险标志物.
研究的目的:
- 评估AI-ECG模型在识别心脏疾病方面的特异性.
- 确定AI-ECG模型是否作为特定疾病分类器或一般心血管风险标记器.
- 评估AI-ECG模型在不同人群和条件中的表型关联.
主要方法:
- 利用电子健康记录 (EHR) 和潜在队列的四个研究群体.
- 部署了六个AI-ECG模型 (五个用于特定的心血管疾病,一个用于性别) 和六个实验性非心血管模型.
- 使用全现象关联研究 (PheWAS) 框架分析了AI-ECG与横截面表型 (逻辑回归) 和新发的心血管疾病 (Cox回归) 的关联.
主要成果:
- 人工智能-心电图模型与它们的目标表型有显著的关联,但也与广泛的其他心血管表型 (几率比率2.16-4.41) 有关.
- 与非心血管模型不同的是,针对各种心血管疾病训练的不同AI-ECG模型中的表型关联模式相似.
- 在全模型,数据集和分析类型 (横截面和前性) 中观察到表型关联的高相关性 (r=0.65-0.99).
结论:
- 人工智能-心电图模型可以检测和预测广泛的心血管疾病,挑战它们作为单纯二进制诊断工具的使用.
- 这些发现支持AI-ECG模型作为更广泛的心血管生物标志物的实用性,而不是特定条件的分类器.
- 这种广泛的关联模式表明AI-ECG在综合性心血管风险评估中可能发挥作用.
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