Medicine and Health

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

C. Bock, J. E. Walter, et al.

This paper reveals how machine learning (ML) can enhance the diagnosis of functionally relevant coronary artery disease (fCAD), outperforming cardiologists and potentially reducing unnecessary imaging procedures. The innovative approaches presented by Christian Bock, Joan Elias Walter, Bastian Rieck, and colleagues could shape the future of cardiac healthcare.

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Abstract

This paper investigates the use of machine learning (ML) to improve the diagnosis of functionally relevant coronary artery disease (fCAD). Two ML approaches are presented: one using eight static clinical variables and the other leveraging electrocardiogram (ECG) signals from exercise stress testing. Results show that ML outperforms cardiologists in predicting fCAD (AUROC: 0.71 vs. 0.64, p = 4.0E-13), potentially reducing imaging procedures by 15–17%. Combining ML with cardiologist judgment further improves performance (AUROC: 0.74).

Publisher

Nature Communications

Published On

Jun 12, 2024

Authors

Christian Bock, Joan Elias Walter, Bastian Rieck, Ivo Strebel, Klara Rumora, Ibrahim Schaefer, Michael J. Zellweger, Karsten Borgwardt, Christian Müller

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