External Validation of Artificial Intelligence-enabled Electrocardiography (AI-ECG) for the Detection of Left Ventricular Dysfunction (LVD)
1 other identifier
observational
12,500
1 country
13
Brief Summary
This is a multi-center, retrospective study evaluating the performance of an artificial intelligence-enabled electrocardiography (AI-ECG) algorithm in detecting reduced left ventricular ejection fraction (LVEF ≤ 40%). All included patients from participating hospitals must have undergone a digital 12-lead electrocardiogram (ECG) and an echocardiogram with assessment of LVEF within seven days. The AI-ECG algorithm will be applied to evaluate its diagnostic performance, which will be further assessed across subgroups stratified by demographic characteristics and clinical factors.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Aug 2025
Shorter than P25 for all trials
13 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
June 16, 2025
CompletedFirst Posted
Study publicly available on registry
June 26, 2025
CompletedStudy Start
First participant enrolled
August 1, 2025
CompletedPrimary Completion
Last participant's last visit for primary outcome
August 31, 2025
CompletedStudy Completion
Last participant's last visit for all outcomes
September 30, 2025
CompletedJune 26, 2025
June 1, 2025
1 month
June 16, 2025
June 25, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
The Sensitivity and specificity of AI-ECG model for left ventricular ejection fraction ≤ 40%
The primary objective of the study was to evaluate the sensitivity and specificity of the artificial intelligence-enabled electrocardiography (AI-ECG) model in detecting left ventricular dysfunction, defined as left ventricular ejection fraction (LVEF) ≤ 40% as confirmed by transthoracic echocardiography.
within 7 days
Interventions
AI-ECG Algorithm to detect LVEF\<=40%
Eligibility Criteria
All patients with ECGs and an echocardiogram within 7 days
You may qualify if:
- patients with ECGs and an echocardiogram within 7 days
You may not qualify if:
- Missing ECG signals
- Missing LVEF assessment in echocardiograms
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (13)
Hualien Armed Forces General Hospital
Hualien City, Taiwan
Kaohsiung Armed Forces General Hospital Gangshan Branch
Kaohsiung City, Taiwan
Kaohsiung Armed Forces General Hospital
Kaohsiung City, Taiwan
Zuoying Armed Forces General Hospital
Kaohsiung City, Taiwan
Tri-Service General Hospital Keelung Branch
Keelung, Taiwan
Tri-Service General Hospital Penghu Branch
Pengfu, Taiwan
Kaohsiung Armed Forces General Hospital Pingtung Branch
Pingtung City, Taiwan
Taichung Armed Forces General Hospital Zhongqing Branch
Taichung, Taiwan
Taichung Armed Forces General Hospital
Taichung, Taiwan
Tri-Service General Hospital Beitou Branch
Taipei, Taiwan
Tri-Service General Hospital Songshan Branch
Taipei, Taiwan
Taoyuan Armed Forces General Hospital Hsinchu Branch
Taoyuan District, Taiwan
Taoyuan Armed Forces General Hospital
Taoyuan District, Taiwan
Related Publications (1)
Chen HY, Lin CS, Fang WH, Lou YS, Cheng CC, Lee CC, Lin C. Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis. J Pers Med. 2022 Mar 13;12(3):455. doi: 10.3390/jpm12030455.
PMID: 35330455BACKGROUND
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Clinical Doctor, Principal Investigator
Study Record Dates
First Submitted
June 16, 2025
First Posted
June 26, 2025
Study Start
August 1, 2025
Primary Completion
August 31, 2025
Study Completion
September 30, 2025
Last Updated
June 26, 2025
Record last verified: 2025-06
Data Sharing
- IPD Sharing
- Will not share