Abstract 4137726: A Deep Learning Digital Biomarker for Mitral Valve Prolapse using Echocardiogram Videos

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Background: Mitral valve prolapse (MVP) has a prevalence of 2–3% and is a risk factor for heart failure and sudden death, but MVP diagnosis by transthoracic echocardiography (TTE) requires time and clinical expertise. We trained a deep learning model to classify MVP from TTE videos. Methods: DROID-MVP is a convolutional neural network trained to classify MVP using echocardiographer labels using 973,531 digital videos (parasternal long axis and apical views) from 45,657 studies performed in 15,728 cardiology patients at Massachusetts General Hospital. We validated DROID-MVP in 1,726 cardiology patients (4,869 studies) and 8,903 primary care patients (8,903 studies), and tested associations between predicted MVP score (range 0-1) and mitral regurgitation (MR) severity and left atrial (LA) anterior-posterior diameter in primary care patients with available measurements. Results: Of 15,728 patients (6,029 [38%] women; mean age at first TTE 61 ± 17 years) in the training set, 729 (4.6%) had at least 1 study with MVP. DROID-MVP identified MVP in both the cardiology (area under the receiver operating characteristic curve [AUC] 0.955 [95% confidence interval: 0.939-0.970]; average precision [AP] 0.716 [0.649-0.776]; prevalence 0.035) and primary care (AUC 0.966 [0.955-0.978]; AP 0.668 [0.601-0.730]; prevalence 0.022) test sets ( Figure 1 ). Discrimination persisted across strata of MR severity (AUC range 0.877-0.987). High (>0.67) vs low (<0.33) MVP score was associated with higher odds of moderate/severe MR (odds ratio 20.1 [12.2-33.4], p<0.001) and higher LA diameter (2.7 [0.9-4.4] mm, p<0.001; Figure 2 ), after adjusting for age, sex, height, and weight. Conclusions: A deep learning model identifies MVP from TTE videos with excellent discrimination and higher model predictions are associated with greater MR severity and LA size. Our work demonstrates how deep learning can automate MVP diagnosis and may serve as a digital biomarker of MVP-associated structural abnormalities.