AI ECG Algorithm for Detecting LV Systolic Dysfunction

June 8, 2026 updated by: Moon-Seung Soh, Ajou University School of Medicine

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

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Left ventricular systolic dysfunction (LVSD) is associated with an increased risk of heart failure, hospitalization, and mortality. Although transthoracic echocardiography is the standard method for assessing left ventricular ejection fraction (LVEF), its widespread use as a screening tool is limited by availability, cost, and the need for specialized personnel. Artificial intelligence (AI)-based electrocardiography (ECG) algorithms have emerged as promising tools for identifying patients with reduced LVEF using routinely acquired ECG signals.

DeepECG LVSD is a deep learning-based ECG algorithm developed to detect LVSD (LVEF ≤40%) from standard 12-lead ECG recordings. Previous retrospective validation studies demonstrated high diagnostic performance; however, prospective clinical validation in real-world practice remains limited.

The purpose of this prospective observational cohort study is to evaluate the diagnostic performance and clinical utility of DeepECG LVSD in adult patients undergoing both ECG and transthoracic echocardiography at Ajou University Hospital. Approximately 15,000 patients aged 19 years or older who have undergone ECG and echocardiography within 30 days will be enrolled.

The primary objective is to assess the accuracy of the AI algorithm for detecting LVSD using echocardiographic LVEF as the reference standard. Diagnostic performance will be evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy.

Secondary objectives include evaluating the association between AI-predicted LVSD and short-term clinical outcomes, including 30-day all-cause mortality, emergency department visits, and heart failure rehospitalization. Exploratory subgroup analyses will assess algorithm performance according to demographic and clinical characteristics, including age, sex, heart failure status, chronic kidney disease, hypertension, diabetes mellitus, and the interval between ECG and echocardiography.

This study is designed as a minimal-risk observational study and will provide prospective evidence regarding the effectiveness of AI-enabled ECG screening for LVSD in routine clinical practice. Findings from this study may support broader implementation of AI-based ECG tools for the early identification of patients at risk for heart failure and reduced left ventricular systolic function.

Study Type

Observational

Enrollment (Estimated)

15000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • South Korea
    • Gyeonggi-do
      • Suwon, Gyeonggi-do, South Korea, 16499
        • Recruiting
        • Ajou University School of Medicine
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

Adult patients aged 19 years or older undergoing routine clinical care at Ajou University Hospital who have both transthoracic echocardiography and 12-lead electrocardiography (ECG) performed within 30 days. Participants may be recruited from outpatient clinics, inpatient wards, or the emergency department.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Adults aged ≥19 years with ECG and echocardiography performed within 30 days
Adult patients aged 19 years or older who underwent both transthoracic echocardiography and electrocardiography (ECG) within 30 days of each other
Other: None-placebo
There is no intervention group

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
AUROC for detection of LVSD (LVEF ≤40%)
Time Frame: During procedure
Diagnostic performance including AUROC, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.
During procedure

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Ajou University School of Medicine

Collaborators

VUNO Inc.

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2026

Primary Completion (Estimated)

December 31, 2027

Study Completion (Estimated)

December 31, 2027

Study Registration Dates

First Submitted

June 4, 2026

First Submitted That Met QC Criteria

June 4, 2026

First Posted (Actual)

June 9, 2026

Study Record Updates

Last Update Posted (Actual)

June 10, 2026

Last Update Submitted That Met QC Criteria

June 8, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AJOUIRB-OB-2026-001

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual participant data will not be publicly shared due to patient privacy and institutional data protection policies.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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