Algorithm for predicting valvular heart disease from heart sounds in an unselected cohort - Waaler, Per Niklas; Melbye, Hasse; Schirmer, Henrik; Johnsen, Markus Kreutzer; Donnem, Tom; Ravn, Johan; Andersen, Stian; Davidsen, Anne Herefoss; Aviles Solis, Juan Carlos; Stylidis, Michael; Bongo, Lars Ailo (Frontiers in Cardiovascular Medicine, 2024)
Objective To assess the ability of state-of-the-art machine learning algorithms to detect valvular heart disease (VHD) from digital heart sound recordings in a general population that includes asymptomatic cases and intermediate stages of disease progression. Methods We trained a recurrent neural network to predict murmurs from heart sound audio using annotated recordings collected with digital stethoscope from 4 auscultation positions in 2124 participants from the Tromsø7 study. The predicted murmurs were used to predict VHD as determined by echocardiography. Results 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. AUC for predicting symptomatic cases was higher for AR and MR, 0.756 and 0.711 respectively. Screening jointly for symptomatic regurgitation or presence of stenosis resulted in AUC 0.86, with 97.7% of AS cases (n=44) and all 12 MS cases detected.