Improvement of an Algorithm to Detect Structural Heart Murmurs in Adult Patients Using Electronic Stethoscopes
Eko Devices, Inc.
Summary
The main objective of this study is to evaluate a machine learning model's ability to detect murmurs indicative of structural heart disease ("structural murmur") by analyzing phonocardiogram waveforms-and simultaneous electrocardiogram waveforms when available-in multiple auscultatory positions per subject. Diagnosis of structural murmur will be confirmed by gold-standard echocardiography and reviewed by an expert panel of cardiologists.
Eligibility
- Age range
- 18+ years
- Sex
- All
- Healthy volunteers
- No
Inclusion Criteria: * 18+ years old * Patient or patient's legal healthcare proxy consents to participation * Documented history of SHD * Undergoing (or has undergone, within 30 days) a complete echocardiogram * Willing to have heart recordings done with two different electronic stethoscopes Exclusion Criteria: * Patient or proxy is unwilling/unable to give written informed consent * Unable to complete a complete echocardiogram, or none recent completed within the last 30 days * No documented history of SHD * Experiencing a known or suspected acute cardiac event * Mechanical ventricular sup…
Interventions
- DeviceEko digital stethoscopes
Use of the Eko CORE 500 digital stethoscope and 3M Littmann CORE Digital Stethoscope to auscultate and record cardiac phonocardiogram and (when available) electrocardiogram waveforms, as well as heart sounds.
Location
- Cox Medical CentersSpringfield, Missouri