Deep Learning Algorithm Predicts Angiographic Coronary Artery Disease in Stable Patients Using Only a Standard 12-Lead Electrocardiogram

Abstract Background Current electrocardiogram analysis algorithms cannot predict the presence of coronary artery disease (CAD), especially in stable patients. This study assessed the ability of an artificial intelligence algorithm (ECGio; HEARTio Inc, Pittsburgh, PA) to predict the presence, location, and severity of coronary artery lesions in an unselected stable patient population. Methods A cohort of 1659 stable outpatients was randomly divided into training (86%) and validation (14%) subsets, maintaining population characteristics. ECGio was trained and validated using electrocardiograms paired with retrospectively collected angiograms. Coronary artery lesions were classified in 2 analyses. The primary classification was no to mild ( Results In the primary analysis, 22 patients had no angiographic CAD and were grouped mild CAD (56 patients, DS Conclusions This study strongly suggests that it is possible to use an artificial intelligence algorithm to determine the presence and severity of CAD in stable patients, using data from a 12-lead electrocardiogram.