Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline-recommended CAC score categories from clinical features and surface electrocardiograms.
See the article: OP-EHJD200009 51..61 (heartsciences.com)
