Deep Learning to Estimate Left Ventricular Ejection Fraction From Routine Coronary Angiographic Images

Abstract

BackgroundCine images during coronary angiography contain a wealth of information besides the assessment of coronary stenosis. We hypothesized that deep learning (DL) can discern moderate-severe left ventricular dysfunction among patients undergoing coronary angiography. ObjectivesThe purpose of this study was to assess the ability of machine learning models in estimating left ventricular ejection fraction (LVEF) from routine coronary angiographic images. MethodsWe developed a combined 3D-convolutional neural network (CNN) and transformer to estimate LVEF for diagnostic coronary angiograms of the left coronary artery (LCA). Two angiograms, left anterior oblique (LAO)-caudal and right anterior oblique (RAO)-cranial projections, were fed into the model simultaneously. The model classified LVEF as significantly reduced (LVEF ≤40%) vs normal or mildly reduced (LVEF>40%). Echocardiogram performed within 30 days served as the gold standard for LVEF. ResultsA collection of 18,809 angiograms from 17,346 patients from Mayo Clinic were included (mean age 67.29; 35% women). Each patient appeared only in the training (70%), validation (10%), or testing set (20%). The model exhibited excellent performance (area under the receiver operator curve [AUC] 0.87; sensitivity 0.77; specificity 0.80) in the training set. The model’s performance exceeded human expert assessment (AUC, sensitivity, and specificity of 0.86, 0.76, and 0.77, respectively) vs (AUC, sensitivity, and specificity of 0.76-0.77, 0.50-0.44, and 0.90-0.93, respectively). In additional sensitivity analyses, combining the LAO and RAO views yielded a higher AUC, sensitivity, and specificity than utilizing either LAO or RAO individually. The original model combining CNN and transformer was superior to DL models using either 3D-CNN or transformers. ConclusionsA novel DL algorithm demonstrated rapid and accurate assessment of LVEF from routine coronary angiography. The algorithm can be used to support clinical decision-making and form the foundation for future models that could extract meaningful data from routine angiography studies.