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, Caroline Currie, Catherine Brooks, Brent E. White, Anna Huskin, Jason Paek, Jack Geocaris, Dinatu Elnathan, Ria Ronquillo, Roy Kim, Zenith H. Alam, Vaikom S. Mahadevan, Sophie G. FullerGrant W. Stalker, Sara A. Bravo, Dina Jean, John J. Lee, Medeona Gjergjindreaj, Christos G. Mihos, Steven T. Forman, Subramaniam Venkatraman, Patrick M. McCarthy, James D. Thomas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

BACKGROUND: Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. METHODS AND RESULTS: Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the ap-propriate anatomic auscultation location detected moderate-to-severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate-to-severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. CONCLUSIONS: The deep learning algorithm’s ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. REGISTRATION: URL: https://clini​caltr​ials.gov; Unique Identifier: NCT03458806.

Original languageEnglish (US)
Article numbere019905
JournalJournal of the American Heart Association
Volume10
Issue number9
DOIs
StatePublished - May 4 2021

Keywords

  • Auscultation
  • Machine learning
  • Neural networks
  • Physical examination
  • Valvular heart disease

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Fingerprint

Dive into the research topics of 'Deep learning algorithm for automated cardiac murmur detection via a digital stethoscope platform'. Together they form a unique fingerprint.

Cite this