Dec 2023 | eMurmur Heart AI enables detection of heart valve disease in the community pharmacy setting
Background: Cardiovascular disease causes a quarter of all deaths in the UK, making it a top NHS priority. One such cardiovascular disease, heart valve disease (HVD), is serious with worse outcomes than many cancers. Yet diagnosed early, before a patient deteriorates, HVD can be treated effectively with valve replacement or repair. A large proportion of patients however are estimated to be undiagnosed or untreated each year in England. By using digital auscultation with eMurmur® stethoscope artificial intelligence (AI), community pharmacists have the potential to assist with and increase the detection of HVD.
Methods: Pharmacist carried out digital stethoscope auscultation and where systolic murmur was detected, patients were referred to a community clinic for full compensation echocardiography.
Results: This service evaluation demonstrates that community pharmacists can identify undiagnosed HVD, in this case referring more patients with non-trivial HVD to echocardiography than GPs. The rapid data sharing enabled by this technology supports quick decision-making for patients with critical disease requiring immediate treatment. Pharmacists can therefore play an important role as a gateway to accessing potentially lifesaving treatment. Patients appeared comfortable attending a digital auscultation service in a community pharmacy setting. Such a proactive service may help reduce the number of people with severe HVD who go undiagnosed and untreated.
Oct 2021 | Johns Hopkins presents assessment of the eMurmur home-based remote auscultation technology at High Value Practice Academic Alliance National Conference
Background: With the constraints placed on in-person clinical activities during the COVID-19 pandemic and the rapid switch to video visits, we had the opportunity to test the feasibility of AI-assisted home-based remote cardiac auscultation during the telemedicine visit. This form of “enhanced” telemedicine (ETM) has not been well studied to date. Unnecessary in-person cardiology referrals and echocardiography overuse lead to financial, logistical, and emotional burdens for pediatric patients and their families which may be addressed by in-home ETM visits, even beyond the pandemic.
Objective: Assess feasibility and usefulness of in-home remote auscultation (RA) for pediatric patients referred for new or follow-up cardiology evaluation as an enhancement to the telemedicine video visit to potentially avoid unnecessary in-person visits or echocardiography.
Methods: 47 patients scheduled for new or follow-up pediatric cardiology evaluation by video visit were recruited, consented verbally by an IRB-approved script, and enrolled.
Prior to the scheduled video visit, families were sent a smartphone with the eMurmur Connect app and a pre-paired electronic stethoscope. The cardiologist guided equipment usage, directing the patient, or parent/guardian on how to hold and move the stethoscope. The cardiologist was able to listen remotely to the heart sounds in real time and make recordings, from which an AI algorithm derived signal-based data including heart rate and murmur characteristics.
Patients, families, and the cardiologist were asked to complete a Qualtrics survey after the visit to rate their experiences. The patient and family survey was anonymous, while the cardiologist
data was visit-specific.
Results: Out of 47 visits in which RA was attempted, 42 (91%) resulted in at least one recording and were considered adequate for clinical decision-making. 10-15 minutes was the amount of time most often spent on RA during a visit. Of the 43 visits where auscultation quality was considered adequate for clinical decision-making, RA was considered helpful in 42 (98%). Of those 42, 45% showed no changes from previous exams, saving patients and families from an additional clinic visit and echocardiogram, and 14% showed changes from previous exams, prompting an in-person visit and follow-up echocardiogram. 14% had new diagnoses manageable without an in-person visit.
For patients, ETM was reported to be easy, valuable, and to increase child and family comfort from multiple perspectives (privacy, COVID-19 exposure, etc). For key patient experience metrics, >90% of patients and their families rated the ETM visit as on par with or better than an in-person visit.
One parent commented: “This process has been very well thought out and seems to work wonderfully [...] everything is very user friendly and I do feel that the children respond well with not being in a strange office setting.”
Conclusions: In-home RA is feasible and holds value for patients and clinicians to potentially reduce unnecessary in-person visits and echocardiograms. This form of ETM is time- and resource-efficient, and shows particular promise for patients living in rural or other areas with limited access to specialty care, patients or families who may need flexibility around work and school commitments, and patients who are particularly vulnerable to pathogens.
Sep 2020 | Stanford University evaluates the implementation and utility of eMurmur University for teaching auscultation skills to incoming pediatric cardiology fellows
Background: Mastery of cardiac auscultation is a core component of cardiology training. New computer-based auscultation teaching tools have been developed to help teach this skill to trainees.
Objective: To assess incoming pediatric cardiology fellows’ auscultation skills and evaluate a phone-based application (app) to teach auscultation.
Conclusions: The use of an app-based teaching tool is an effective method to teach auscultation skills to incoming pediatric cardiology fellows. The app can improve fellows’ comfort with identifying the most common lesions seen in pediatric cardiology.
May 2020 | Feasibility of using an artificial intelligence-enabled stethoscope and telemedicine to improve referrals and reduce inappropriate use of echocardiography in children with heart murmurs
Objective: Assess the feasibility of using an electronic stethoscope with eMurmur Heart AI and remote cardiologist interpretation of recorded heart sounds to screen for pathologic murmurs.
Results: Among 137 subjects, 96% were successfully recorded (80 male, average age 6.1 years, SD 6.1, 5 not recorded due to network connectivity or failed quality check). Of the 132 obtained, 5 recordings were uninterpretable (1 by AI and 4 by remote cardiologist due to noise). Overall, net feasibility was 93%. 53 subjects were referred to the cardiology clinic by primary providers because of a murmur, of which 10 had pathology. For these, accuracy of the AI algorithm was similar to that of the remote cardiologist.
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 improve clinical outcomes for patients with heart murmurs when compared to current referral behavior and outcomes.
Conclusions: Accuracy of the 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.
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.
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.
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%.
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.
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.
