Traditional cardiac auscultation relies 100% on the subjective interpretation of heart sounds by the examiner.
eMurmur AI puts expert level heart murmur detection capabilities into the hands of primary care providers.
Heart sounds are recorded via the eMurmur Connect app which receives the sounds from an electronic stethoscope. During recording a real-time signal quality check ensures that good recording quality is achieved.
The recorded data is transmitted to the eMurmur cloud for AI analysis. Results (murmur type, heart rate, S1/S2 markers) are returned to the eMurmur Connect app within seconds. All data is encrypted at rest and in transit.
An electronic record for each patient session is created in the eMurmur Connect Web Portal. Users can review heart and lung recordings, eMurmur AI findings, provider notes, patient history and more at any time from anywhere.
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.
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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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"The evidence for its utility is solid and includes five studies on over 1000 patients, including blinded, multi-centre trials. This system has been shown to be accurate, informative, and easy-to-use. It provides healthcare professionals with a potent screening tool and method to confirm their clinical diagnoses, enhancing patient care."
Dr. Derek Exner, Professor and Canada Research Chair in Cardiovascular Clinical Trials
University of Calgary
“eMurmur AI is so relatively simple, it is so intuitive, that the degree of training required to provide a very high level of certainty as to whether a patient has or does not have cardiac disease puts it into a very different category from other technologies.“
Dr. Andrew Redington, Executive Co-director and Chief of Pediatric Cardiology
Cincinnati Children's Hospital
