eMurmur ID
AI-based heart murmur detection solution

FDA cleared & CE marked

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How eMurmur ID works

Record heart sounds on mobile device

Takes 15-20 seconds, includes real-time signal quality check. Sends data to cloud for analysis.

Receive AI analysis on mobile device

Takes less than 3 seconds to receive results on mobile device.

Access results, recordings & documentation from anywhere

Access to web portal for patient management and electronic documentation.

Request and receive expert consultation

Securely share patient entries for expert consultation and telemedicine.

Click to test your murmur recognition!

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Heart murmur detection:
the past vs future

Heart disease is the leading cause of death globally for both men and women, causing 31% of all deaths annually. Heart auscultation (meaning “to listen with a stethoscope”) is a 200+ year-old method to screen for heart disease by listening for abnormal heart sounds (murmurs), used worldwide for patients of all ages. It’s an integral step in providing primary care. However, heart auscultation is subjective in nature and studies have shown accuracy rates amongst primary care providers to be as low as 20-25%. With an accuracy rate of over 85%, eMurmur ID makes expert-level auscultation accessible to any healthcare provider performing auscultation without the need for specialty training.

The technology

eMurmur ID uses advanced machine learning to identify and classify pathologic and innocent heart murmurs, the absence of a heart murmur, the heart rate and S1/S2 markers. The end-to-end solution is comprised of AI-based analytics, a mobile app, and a web portal – all FDA cleared, HIPAA compliant and CE marked. It supports the workflows of healthcare providers performing cardiac auscultation and has multiple applications including primary and specialty care, telemedicine, and corporate health.

Key features

End-to-end mobile and cloud based software solution


App records heart sounds and displays results in seconds


AI engine detects and classifies heart murmurs with high accuracy


Clinical Evidence

Validated through prospective clinical trials, blinded and multi-centered


Web portal for remote 24/7 access, expert consultations, and PDF reporting



Reimbursement under telemedicine and chronic care management codes


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Clinical work

Clinical studies completed: 5
Clinical studies ongoing: 3
Data from patients analyzed: 1,000+
Heart sound recordings analyzed: 3,000+
Heart murmurs classified: 1,500+
Heart beats analyzed: 50,000+
Heart rates ranges detected: 50-180 bpm
Patient populations: newborns, children, youth, adults, elderly
Clinically observed sensitivity: 85.0%
Clinically observed specificity: 86.7%

Papers & abstracts

Mar 2020 | Clinical data on using eMurmur ID and telemedicine to improve referrals in children with heart murmurs to be presented at the PAS 2020 Meeting

Data from a telemedicine innovation study lead by Dr. Reid Thompson, Johns Hopkins Medicine, will be presented at the Pediatric Academic Societies Meeting in Philadelphia (April 29-May 6, 2020). The study involved 137 subjects and yielded a net feasibility of 93%, concluding that a new paradigm of screening heart murmurs with an AI-enabled electronic stethoscope and remote listening by a cardiologist was feasible and accurate.
Conference link

Nov 2019 | Clinical data from Johns Hopkins telemedicine study presented at 2019 Architecture of High Value Health Care Conference

Objectives: This study explores whether incorporating an artificial intelligence (AI) algorithm (eMurmur ID) and telemedicine to assist referral decision-making can decrease the number of inappropriate echocardiograms and improved clinical outcomes for patients with heart murmurs when compared to current referral behavior and outcomes.

Conclusions: Accuracy of AI-algorithm is similar to a trained cardiologist (132 patients, 84% accuracy). A new paradigm to screen for pathologic heart murmurs in primary care settings is feasible and could reduce the number of patients with only innocent heart murmurs being referred inappropriately to cardiology or echocardiography by up to 50-75%. It could help confirm the clinician’s suspicion of important pathologic heart murmurs while providing evidence to reassure parents of children with innocent murmurs that their children’s hearts are normal.
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Dec 2018 | Johns Hopkins study demonstrates 88% accuracy of eMurmur, as published in Pediatric Cardiology

Results from a study called "Artificial Intelligence-Assisted Auscultation of Heart Murmurs: Validation by Virtual Clinical Trial" have been published in the peer-reviewed journal of Pediatric Cardiology. This is the first reported comprehensive and objective evaluation of an AI-based murmur detection algorithm, and included 3180 heart sound recordings from 603 outpatient visits. Algorithm estimation of heart rate was similar to gold standard. Sensitivity and specificity for detection of pathologic cases were 93% (CI 90–95%) and 81% (CI 75–85%), respectively, with accuracy 88% (CI 85–91%). In conclusion, the eMurmur algorithm tested has high sensitivity and specificity for detection of pathologic murmurs, similar to levels reported for specialist auscultation, making it a potentially useful screening tool for heart disease.
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Nov 2017 | Data from comparative clinical study presented at AHA Scientific Sessions 2017

Dr. Lillian Lai of Children’s Hospital Eastern Ottawa presented a study today titled “Performance Evaluation of Two Heart Murmur Detection Applications by Prospective Clinical Trial” at the AHA Scientific Session 2017 today.
The study included 93 patients and demonstrated a sensitivity and specificity for automated detection of pathologic murmurs of 84.4% and 85.9%, respectively, with the gold standard being echocardiography, with eMurmur significantly outperform the other device.
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Nov 2017 | Results from Johns Hopkins study presented at AHA Scientific Sessions 2017

In the AHA Scientific Session “Best Abstracts in Health Tech ”, Dr. W. Reid Thompson, associate professor of pediatrics at Johns Hopkins University, today presented a study titled “Validation of a Heart Murmur Algorithm by Virtual Clinical Trial” which used the eMurmur algorithm to blindly analyze 3,180 heart sound recordings from the Johns Hopkins Cardiac Auscultatory Recording Database (CARD). Study results showed a sensitivity of 93% and a specificity of 81%, as well as an accuracy of 88%.
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July 2017 | eMurmur’s heart murmur detection algorithm scores 89% sensitivity in an elderly population clinical trial

eMurmur announces completion of a clinical trial evaluating the performance of its heart murmur detection algorithms on an elderly population with congenital heart disease. The algorithm’s sensitivity for autonomous detection of pathologic murmurs was 89%. No high severity cases were missed by the algorithm. Further, it yielded accurate heart rate estimation and S1/S2 detection, despite the presence of significant environmental noise.
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Nov 2016 | eMurmur’s heart murmur detection algorithm scores 94% accuracy in clinical pilot study

eMurmur announces impressive pilot study of its AI platform on 106 children at Children’s Hospital of Eastern Ontario, Canada, where it differentiated pathologic from innocent heart murmurs with high sensitivity (87%) and specificity (100%), a positive predictive value of 100%, a negative predictive value of 90%, and high accuracy (94%) when compared with echocardiography as the gold standard for diagnosing murmurs.
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