Machine learning to assess coronary artery disease status—is it helpful?

Over the past decade, massive, biologically rich datasets—and machine learning methods that can interrogate them—have allowed for the development of predictive cardiovascular disease diagnostics. These machine learning markers are designed to identify patients with undiagnosed disease or those at risk of future disease, often earlier and with better predictive accuracy than conventional guidelines. 1 Attia ZI Noseworthy PA Lopez-Jimenez F et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019; 394: 861-867 Summary Full Text Full Text PDF PubMed Scopus (543) Google Scholar , 2 Pirruccello JP Chaffin MD Chou EL et al. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet. 2022; 54: 40-51 Crossref PubMed Scopus (40) Google Scholar , 3 Ulloa-Cerna AE Jing L Pfeifer JM et al. rECHOmmend: an ECG-based machine learning approach for identifying patients at increased risk of undiagnosed structural heart disease detectable by echocardiography. Circulation. 2022; 146: 36-47 Crossref PubMed Scopus (7) Google Scholar Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohortsElectronic health record-based machine learning was used to generate an in-silico marker for coronary artery disease that can non-invasively quantify atherosclerosis and risk of death on a continuous spectrum, and identify underdiagnosed individuals. Full-Text PDF