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Open Access Gold as soon as Publ. Version/Full Text is submitted to ZB.
Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning.
Nat. Commun. 15:5034 (2024)
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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Publication type
Article: Journal article
Document type
Scientific Article
ISSN (print) / ISBN
2041-1723
e-ISSN
2041-1723
Journal
Nature Communications
Quellenangaben
Volume: 15,
Issue: 1,
Article Number: 5034
Publisher
Nature Publishing Group
Publishing Place
London
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of AI for Health (AIH)