AI ECG Algorithm for Detecting LV Systolic Dysfunction

NCT07636759 | Recruiting DMC

• 1 other identifier: AJOUIRB-OB-2026-001
• Study Type: observational
• Target: 15,000
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Heart failure; ECG; Artificial Intelligence; Deep Learning

Outcome Measures

Primary Outcomes (1)

• AUROC for detection of LVSD (LVEF ≤40%)

  Diagnostic performance including AUROC, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.

  During procedure

Study Arms (1)

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

Interventions

None-placebo OTHER

There is no intervention group

Adults aged ≥19 years with ECG and echocardiography performed within 30 days

Eligibility Criteria

• Age: 19 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Ajou University School of Medicine: lead
• VUNO Inc.: collaborator

Study Sites (1)

Ajou University School of Medicine

Suwon, Gyeonggi-do, 16499, South Korea

RECRUITING

Related Publications (3)

• Lopez-Jimenez F, Alger HM, Attia ZI, Barry B, Chatterjee R, Dolor R, Friedman PA, Greene SJ, Greenwood J, Gundurao V, Hackett S, Jain P, Kinaszczuk A, Mehta K, O'Grady J, Pandey A, Pullins C, Puranik AR, Ranganathan MK, Rushlow D, Stampehl M, Subramanian V, Vassor K, Zhu X, Awasthi S. A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods. Am Heart J Plus. 2025 Mar 21;54:100528. doi: 10.1016/j.ahjo.2025.100528. eCollection 2025 Jun.

  PMID: 40276542 RESULT

• Choi J, Lee S, Chang M, Lee Y, Oh GC, Lee HY. Author Correction: Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction. Sci Rep. 2022 Oct 13;12(1):17191. doi: 10.1038/s41598-022-22012-7. No abstract available.

  PMID: 36229570 RESULT

• Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019 Jan;25(1):70-74. doi: 10.1038/s41591-018-0240-2. Epub 2019 Jan 7.

  PMID: 30617318 RESULT

MeSH Terms

Conditions

Heart Failure

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Central Study Contacts

MOONSEUNG SOH, MD

CONTACT

+82-31-219-5111; mssoh7701@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Month
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: June 4, 2026
• First Posted: June 9, 2026
• Study Start: January 1, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: June 10, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)