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Improvement of an Algorithm to Detect Structural Heart Murmurs in Adult Patients Using Ele

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 o

Condition(s)Structural Heart Disease
StatusRecruiting
Study typeObservational
SummaryThe 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.
Who can participateInclusion 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 support (such as ECMO, LVAD, RVAD, BiVAD, Impella, intra-aortic balloon pumps, TAH, VentrAssist, DuraHeart, HVAD, EVAHEART LVAS, HeartMate, Jarvik 2000) * Unwilling or unable to follow or complete study pro
Ages18 Years
SexAll
Lead sponsorEko Devices, Inc.
LocationsSpringfield, Missouri, United States
Start date2025-09
NCT IDNCT07202104
Official listinghttps://clinicaltrials.gov/study/NCT07202104

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