Deep Learning for Detection of Pulmonary Hypertension and Reduced Left Ventricular Ejection Fraction Using a Combined Digital Stethoscope and Three-lead Electrocardiogram

Summary

This is a prospective, observational study evaluating whether heart sounds (phonocardiograms) and three-lead electrocardiograms (ECGs) recorded using the Eko CORE 500 digital stethoscope can help detect pulmonary hypertension (PH) and low left ventricular ejection fraction (EF ≤ 40%). PH is a condition characterized by high blood pressure in the pulmonary arteries, which can lead to heart failure and carries significant risks if undiagnosed. Low EF, which indicates reduced pumping ability of the heart, is also associated with increased risk of severe cardiac events but can remain undetected because patients often have no symptoms or only nonspecific symptoms. In this study, adults undergoing clinically indicated echocardiograms at outpatient sites will be invited to participate. Participants will complete a single study session lasting about 20 minutes, during which heart sounds and a three-lead ECG will be collected using the Eko CORE 500 device. If participants have had a clinical 12-lead ECG within 30 days of their echocardiogram, those data may also be used for analysis. The echocardiogram performed as part of routine care within seven days before or after the Eko CORE 500 recording will serve as the reference standard to confirm the presence or absence of PH and low EF. Up to 3,850 participants may be enrolled across multiple sites to ensure that approximately 3,500 complete the study. The data collected will be used to develop and validate artificial intelligence (AI) algorithms that aim to detect PH and identify low EF, potentially enabling earlier and simpler screening for these conditions in clinical practice.

Description

Eligibility

Age range

18+ years

Sex

All

Healthy volunteers

Not specified

Interventions

• Device
  Eko CORE 500 Digital Stethoscope

  The FDA-cleared Eko CORE 500 digital stethoscope is used to collect phonocardiogram (PCG) and three-lead ECG recordings from participants. This observational study uses these recordings to develop and validate artificial intelligence algorithms to detect pulmonary hypertension and low left ventricular ejection fraction. No modifications to the device or device functionality are being tested.

Locations (3)