Clinical trials

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 or right heart catheterization 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 or right heart catheterization, those data may also be used for analysis. A clinically indicated echocardiogram or right heart catheterization (RHC) performed 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.

This prospective, observational study will evaluate whether synchronized heart sound (phonocardiogram, PCG) and three-lead electrocardiogram (ECG) recordings (entered as separate interventions in PRS, though collected together in practice) collected with the Eko CORE 500 can help screen for pulmonary hypertension (PH). Adults (≥18 years) undergoing clinically indicated transthoracic echocardiography (TTE) and/or right heart catheterization (RHC) will complete one study visit (\~20 minutes). During the visit, study staff will obtain at least four 15-second CORE 500 recordings (aortic, pulmonic, tricuspid, and mitral areas). The clinical echocardiogram (and RHC, if performed) within ±7 days of the recordings will provide reference labels for the presence and severity of PH; de-identified demographic and clinical data may also be abstracted from the medical record. The primary objective is to develop and validate a software algorithm to detect PH and, where possible, stratify severity using noninvasive PCG+ECG signals. These recordings are investigational data acquisitions for algorithm development only; they are not diagnostic procedures and will not be used for clinical decision-making. Primary performance measures are sensitivity and specificity versus echocardiogram and RHC references. No clinical decisions will be based on the investigational algorithm, and no changes to standard care are required. The study plans to enroll up to \~1,513 participants to obtain approximately 1,375 evaluable datasets across multiple outpatient sites.

The major goal of the study is to determine whether phonocardiography (using the Eko DUO stethoscope which can capture a three lead ECG reading) can present features that relate to the presence of PH diagnosed by echocardiography or right heart catheterization (RHC), and therefore have a potential to assist the provider to suspect PH.

The purpose of this research is to prospectively train and validate an artificial intelligence machine learning (ML) algorithm to detect the presence of adventitious lung sounds in adults. Clinicians will use the Eko CORE and/or Eko CORE 500 device(s) in real clinical settings to collect normal and abnormal lung sounds, as part of standard of care clinical practice, which will then be used to explore an ML algorithm for classifiers for wheeze, coarse crackle, fine crackle, rhonchus, stridor, rales, and cough, as well as determine any correspondences between the type and/or location of adventitious lung sounds and the type of pulmonary conditions as reported by clinicians.

The main objectives of the study are to: train and validate binary classifiers for wheeze, coarse crackle, fine crackle, rhonchus, stridor, rales, and cough, as well as determine correspondence between type/location of adventitious lung sound and type of pulmonary condition.

The main objectives of this study are to train and evaluate an algorithm that predicts whether an individual has an ejection fraction ≤ 40%, using heart sounds and a 3-lead ECG as inputs, as well as determine the impact of gender, age, and race on algorithm performance.

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.