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Algorithm for Predicting Valvular Heart Disease from Heart Sounds in an Unselected Cohort

Background Although neural networks have shown promise in classifying pathological heart-sounds, algorithms have so far either been trained or tested on selected cohorts which can result in selection bias. Herein, the main objective is to explore the ability of neural networks to predict valvular heart disease (VHD) from heart sound (HS) recordings in an unselected cohort. Methods and results Using annotated HSs and echocardiogram data from 2124 subjects from the Tromso 7 study, we trained a recurrent neural network to predict murmur grade, which was subsequently used to predict VHD. Presence of aortic stenosis (AS) was detected with sensitivity 90.9%, specificity 94.5%, and area-under-the-curve (AUC) 0.979 (CI:0.963-0.995). At least moderate AS was detected with AUC 0.993 (CI:0.989-0.997). Moderate or greater aortic and mitral regurgitation (AR and MR) were predicted with AUC 0.634 (CI:0.565-703) and 0.549 (CI:0.506-0.593) respectively, which increased to 0.766 and 0.677 when adding clinical variables as predictors. Excluding asymptomatic cases from the positive class increased sensitivity to AR from 54.9% to 85.7%, and sensitivity to MR from 55.6% to 83.3%. Screening jointly for at least moderate regurgitation or presence of stenosis resulted in detection of 54.1% of positive cases, 60.5% of negative cases, 97.7% of AS cases (n=44), and all 12 MS cases. Conclusions Despite the cohort being unselected, the algorithm detected AS with performance exceeding performance achieved in similar studies based on selected cohorts. Detection of AR and MR based on HS audio was unreliable, but sensitivity was considerably higher for symptomatic cases, and inclusion of clinical variables improved prediction significantly.