Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning

Christian Bock, Joan Elias Walter, Bastian Rieck, Ivo Strebel, Klara Rumora, Ibrahim Schaefer, Michael J. Zellweger, Karsten Borgwardt () and Christian Müller ()
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Christian Bock: ETH Zürich
Joan Elias Walter: University Hospital of Basel, University of Basel
Bastian Rieck: ETH Zürich
Ivo Strebel: University Hospital of Basel, University of Basel
Klara Rumora: University Hospital of Basel, University of Basel
Ibrahim Schaefer: University Hospital of Basel, University of Basel
Michael J. Zellweger: University Hospital of Basel, University of Basel
Karsten Borgwardt: ETH Zürich
Christian Müller: University Hospital of Basel, University of Basel

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract 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

Date: 2024
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DOI: 10.1038/s41467-024-49390-y

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