AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Assessment Using COR ECG Wearable Monitor

Summary

This prospective, multicenter, cluster-randomized controlled study aims to evaluate the accuracy of an investigational artificial intelligence (AI) Software as a Medical Device (SaMD) designed to compute ejection fraction (EF) severity categories based on the American Society of Echocardiography's (ASE) 4-category scale. The software analyzes continuous ECG waveform data acquired by the FDA-cleared Peerbridge COR® ECG Wearable Monitor, an ambulatory patch device designed for use during daily activities.

Shortened from the sponsor's brief summary. View official record on ClinicalTrials.gov

Conditions studied

• Ventricular Ejection Fraction
• LVF
• LV Dysfunction
• Atrial Enlargement
• Conduction Defect
• Heart Failure
• Valvular Heart Disease
• Ischemic Heart Disease
• Cardiotoxicity
• Myocardial Infarction
• Dilated Cardiomyopathy
• HFrEF - Heart Failure With Reduced Ejection Fraction
• HFpEF - Heart Failure With Preserved Ejection Fraction
• Syncope
• Remodeling, Cardiac

Eligibility basics and study details

Study type

Observational

Phase

Not applicable

Ages

18 years and older

Sex

All

Healthy volunteers

Accepted

Sponsor

Peerbridge Health, Inc

Last updated on ClinicalTrials.gov

June 1, 2026

These are the basic criteria only. The full inclusion and exclusion criteria are on the official record, and the study team decides who can take part.

US recruiting sites