在心血管诊断和风险预测中增强人工智能心电图的表型选择性
Philip M Croon1, Lovedeep S Dhingra2, Dhruva Biswas2
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
Circulation
|September 1, 2025
概括
人工智能 (AI) 电心图 (ECG) 模型虽然针对特定情况而设计,但能够识别出广泛的心血管疾病. 这些AI-ECG工具比特定的诊断分类器更好地作为广泛的心血管风险标志物.
科学领域:
- 心脏病学
- 人工智能
- 医疗诊断
背景情况:
- AI-ECG模型通常用于特定的心脏异常.
- 了解它们的表型关联对于临床应用至关重要.
- 本研究调查AI-ECG模型是否是特定疾病或一般心血管风险标志物.
研究的目的:
- 评估AI-ECG模型是否作为特定疾病分类器或更广泛的心血管风险标志物.
- 评估AI-ECG模型的表型关联.
主要方法:
- 使用了来自EHR和潜在队列的数据 (233,689人).
- 部署了六种AI-ECG模型 (五种特定条件,一种性别) 和六种实验性非心血管模型.
- 使用全现象关联研究 (PheWAS) 和物流/Cox回归来分析与横截面和新发现象的关联.
主要成果:
- 所有五种特定条件的AI- ECG模型与心血管表型相关 (ORs 2. 16-4. 41, p<10-6).
- 模型显示了与各种心血管疾病的广泛关联,
- 在不同的AI-ECG模型中,表型关联是相似的,并且在数据集和分析中一致.
结论:
- AI-ECG模型可以检测和预测广泛的心血管疾病,而不仅仅是特定的目标.
- 这种广泛的关联挑战了它们作为二进制诊断工具的使用.
- 这些发现支持AI-ECG模型作为综合性心血管生物标志物的实用性.
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