A Deep Learning Algorithm for Automated Cardiac Murmur Detection Via a Digital Stethoscope Platform

John S Chorba; Avi M Shapiro; Le Le; John Maidens; John Prince; Steve Pham; Mia M Kanzawa; Daniel N Barbosa; Brent E White; Jason Paek; Sophie G Fuller; Grant W Stalker; Sara A Bravo; Dina Jean; Subramaniam Venkatraman; Patrick M McCarthy; James D Thomas

The tested algorithm addresses the need for an effective, reliable, and accessible method to screen for murmurs and ultimately, detect VHD. First, the deep learning algorithm is accurate and reliable, with low inter-operator variability. It shows comparable performance to that of an expert cardiologist, suggesting it could deliver cardiology-level expertise to frontline clinicians in the field. Potential benefits include enabling clinicians to detect VHD earlier and more consistently, and potentially reduce morbidity and mortality due to earlier clinical intervention 29 . Second, the algorithm can detect heart murmurs at the point of care through cellular or wi-fi connectivity with the stethoscope and mobile platform. A tool that can serve as an affordable alternative for diagnosing VHD would be very valuable since echocardiography remains limited by cost, time, and access, especially in rural and underserved areas suffering from a shortage of cardiologists 30 . Third, in light of the recent and ongoing COVID-19 pandemic 31 , the potential to provide expert level diagnostics through telemedicine, thereby limiting the transmission of a highly contagious disease, is also particularly attractive. Furthermore, the digital stethoscope platform employed here could be extended to investigate other auscultation findings, such as lung sounds, which may help improve screening for such disease. Fourth, to the extent the algorithm can accurately exclude valvular disease, it could reduce the burden of unnecessary echocardiography. Overall, our study shows the promise of this tool as an adjunct to clinical care, and illustrates the potential of it expanding into something even greater.