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

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 appropriate 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://ichgcp.net/clinical-trials-registry/NCT03458806" title="See in ClinicalTrials.gov">NCT03458806.

Keywords: auscultation; machine learning; neural networks; physical examination; valvular heart disease.

Conflict of interest statement

Dr Shapiro, Dr Le, Dr Maidens, Dr Prince, Dr Pham, Dr Kanzawa, D.N. Barbosa, C. Curie, C. Brooks, and Dr Venkatraman are employees of Eko. Dr McCarthy receives equity as a member of Eko’s scientific advisory board. Dr Chorba is an unpaid advisor to Eko. The remaining authors have no disclosures to report.

Figures

Figure 1. Predicted and annotated signal quality.

The plot on the right shows that the recordings predicted as “inadequate signal” by the algorithm have low signal quality, as assessed by the cardiologist annotators.

Figure 2. Performance of murmur detection algorithm.

Receiver operating characteristic curves for all recordings (blue) and minimal intensity–filtered murmurs (green) are shown. Eko software operates with parameters yielding the orange marker. Error bars indicate 95% CIs. AUC indicates area under the curve.

Figure 3. Flow of study participants.

We defined valvular heart disease cases as those graded moderate to severe or worse to encompass all levels of disease that could require timely intervention beyond serial monitoring. We defined controls as subjects free of valvular, structural, or congenital heart disease, with no valvular regurgitation or stenosis beyond trivial or physiologic severity. Potential participants included all enrolled subjects (ie, those with recordings). Eligible participants included only those with the appropriate data for analysis. Aortic stenosis (AS) was assessed by a single recording at either the aortic (preferred) or the pulmonic position, and mitral regurgitation (MR) was assessed by a single recording at the mitral position. Actual cases and controls were further filtered from potential cases and controls by removing subjects with “inadequate signal” at the corresponding anatomic locations by the signal quality classifier. Numbers listed in italics represent the subset of annotated recordings.

Figure 4. Performance of aortic stenosis screening by murmur detection algorithm.

The algorithm receiver operating characteristic (ROC) curve is shown in blue. Eko software operates at the orange marker. The performance of the individual cardiologists on the annotated subset of the overall data set is shown by the green, red, and purple markers. Error bars indicate 95% CIs. AUC indicates area under the curve.

Figure 5. Performance of mitral regurgitation screening by murmur detection algorithm.

The algorithm receiver operating characteristic (ROC) curve is shown in blue. Eko software operates at the orange marker. The performance of the individual cardiologists on the annotated subset of the overall data set is shown by the green, red, and purple markers. Error bars indicate 95% CIs. AUC indicates area under the curve.