RecruitingNCT07636759

Prospective Observational Cohort Study of Deep Learning-based ECG Algorithm for Detecting Left Ventricular Systolic Dysfunction

Sponsor: Ajou University School of Medicine

Trial Details

Condition	HF - Heart Failure
Intervention	None-placebo
Status	Recruiting
Participants	15,000
Est. Completion	31 Dec 2027
Last Updated	10 June 2026

Primary Outcome

AUROC for detection of LVSD (LVEF ≤40%)

Eligibility Criteria

Inclusion Criteria

Inclusion Criteria: * Adults aged ≥19 years. * Patients who underwent both transthoracic echocardiography and 12-lead electrocardiography (ECG) at Ajou University Hospital in the outpatient, inpatient, or emergency department setting. * ECG and echocardiography performed within 30 days of each other. Exclusion Criteria: * Interval between ECG and echocardiography greater than 30 days. * Missing or corrupted original ECG waveform data (XML or HL7 format). * Presence of an implanted cardiac device, including a permanent pacemaker, implantable cardioverter-defibrillator (ICD), or cardiac resynchronization therapy (CRT) device. * Missing age, sex, or left ventricular ejection fraction (LVEF) data.

Trial Sites

Countries

South Korea

View Registry Entry

Related Analysis

Major Observational Study Tests AI-Enhanced ECG for Heart Failure Detection — 15,000 Patient Validation of Deep Learning Algorithm

A major 15,000-patient observational study is validating an AI-enhanced ECG algorithm for detecting heart failure with reduced ejection fraction. The DeepECG LVSD system could transform early detection of left ventricular systolic dysfunction in routine clinical practice.

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