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AI ECG Algorithm for Detecting LV Systolic Dysfunction

NCT07636759 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2026-08-07

No results posted yet for this study

Summary

This prospective observational cohort study aims to evaluate the clinical performance of a deep learning-based electrocardiography (ECG) algorithm (DeepECG LVSD) for detecting left ventricular systolic dysfunction (LVSD), defined as left ventricular ejection fraction (LVEF) ≤40%, using transthoracic echocardiography as the reference standard. Approximately 15,000 adult patients undergoing both ECG and echocardiography within 30 days at Ajou University Hospital will be enrolled. Diagnostic performance will be assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Secondary analyses will evaluate the association between AI-predicted LVSD and 30-day clinical outcomes, including all-cause mortality, emergency department visits, and heart failure rehospitalization.

Conditions

  • HF - Heart Failure

Interventions

OTHER

None-placebo

There is no intervention group

Sponsors & Collaborators

  • VUNO Inc.

    collaborator INDUSTRY

  • Ajou University School of Medicine

    lead OTHER

Eligibility

Min Age
19 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-01-01
Primary Completion
2027-12-31
Completion
2027-12-31

Countries

  • South Korea

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07636759 on ClinicalTrials.gov