冠状动脉疾病
计算机科学
疾病
心脏病学
医学
计算生物学
内科学
生物
作者
Christian Bock,Joan Walter,Bastian Rieck,Ivo Strebel,Klara Rumora,Ibrahim Schaefer,Michael J. Zellweger,Karsten Borgwardt,Christian Müller
标识
DOI:10.1038/s41467-024-49390-y
摘要
Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute myocardial infarction. Its early detection is a fundamentally important task in medicine. Classical detection approaches suffer from limited diagnostic accuracy or expose patients to possibly harmful radiation. Here we show how machine learning (ML) can outperform cardiologists in predicting the presence of stress-induced fCAD in terms of area under the receiver operating characteristic (AUROC: 0.71 vs. 0.64, p = 4.0E-13). We present two ML approaches, the first using eight static clinical variables, whereas the second leverages electrocardiogram signals from exercise stress testing. At a target post-test probability for fCAD of <15%, ML facilitates a potential reduction of imaging procedures by 15-17% compared to the cardiologist's judgement. Predictive performance is validated on an internal temporal data split as well as externally. We also show that combining clinical judgement with conventional ML and deep learning using logistic regression results in a mean AUROC of 0.74.
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