Cardiac DiseaseNCT07038018
External Validation of Artificial Intelligence-enabled Electrocardiography (AI-ECG) for the Detection of Left Ventricular Dysfunction (LVD)
An observational study of Cardiac Disease, run by Tri-Service General Hospital (TSGH) at 13 sites in Taiwan.
Trial 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%). Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Cardiac Disease
- What is being tested?
- AI-ECG Algorithm
- How many people
- 12,500 participants
- Where
- 13 sitesTaiwan
- Study dates
- 1 Aug 2025to 30 Sep 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- Adult, Older adult
Study population: All patients with ECGs and an echocardiogram within 7 days
Who may be able to join
- patients with ECGs and an echocardiogram within 7 days
Who cannot join
- Missing ECG signals
- Missing LVEF assessment in echocardiograms
Trial locations and contacts
13 sites in 1 country. Contact the study team or a site near you to ask about taking part.
13 sites in 1 country
Taiwan13 sites
Hualien Armed Forces General Hospital
Hualien City
Kaohsiung Armed Forces General Hospital Gangshan Branch
Kaohsiung City
Kaohsiung Armed Forces General Hospital
Kaohsiung City
Zuoying Armed Forces General Hospital
Kaohsiung City
Tri-Service General Hospital Keelung Branch
Keelung
Tri-Service General Hospital Penghu Branch
Pengfu
Kaohsiung Armed Forces General Hospital Pingtung Branch
Pingtung City
Taichung Armed Forces General Hospital Zhongqing Branch
Taichung
Taichung Armed Forces General Hospital
Taichung
Tri-Service General Hospital Beitou Branch
Taipei
Tri-Service General Hospital Songshan Branch
Taipei
Taoyuan Armed Forces General Hospital Hsinchu Branch
Taoyuan
Taoyuan Armed Forces General Hospital
Taoyuan
About this study
Official title: External Validation of Artificial Intelligence-Enabled Electrocardiograms for the Detection of Reduced Left Ventricular Ejection Fraction
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.
Read the detailed description
Data were collected from 13 hospitals, excluding the medical center that developed the artificial intelligence-enabled electrocardiography (AI-ECG) algorithm. The primary objective of the study was to evaluate the sensitivity and specificity of the AI-ECG model in detecting left ventricular dysfunction, defined as left ventricular ejection fraction (LVEF) ≤ 40%. To ensure clinical applicability, predefined thresholds required both sensitivity and specificity to exceed 0.80 in external validation cohorts. Sample size calculations were based on testing the null hypothesis that sensitivity equals 0.80. In the development hospital cohort, the model demonstrated a sensitivity of 0.869 and a specificity of 0.896. With a two-sided significance level (α) of 0.05 and a power of 90%, an estimated 310 cases of LVEF ≤ 40% were required.
Given that the prevalence of left ventricular dysfunction was 4% in the development hospital cohort but expected to be lower-between 2.5% and 3%-in external validation settings (i.e., regional and local hospitals), the total sample size needed to accrue the target number of cases was estimated to range between 10,333 and 12,400 patients. To achieve this, six regional hospitals and seven local hospitals were selected as external validation sites. Because both electrocardiography and echocardiography were required within a seven-day interval-leading to anticipated exclusions-approximately 1,500 patients were targeted from each regional hospital and 500 from each local hospital, resulting in a final target sample size of approximately 12,500 patients.
- Conditions
- Cardiac Disease
- Being tested
- AI-ECG Algorithm
- Keywords
- left ventricular dysfunction, artificial intelligence
- Medical subject headings
- Heart Diseases, Ventricular Dysfunction, Left
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-ECG AlgorithmDiagnostic test
AI-ECG Algorithm to detect LVEF<=40%
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: within 7 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Jun 2025
- Study start1 Aug 2025 (estimated)
- Primary completion31 Aug 2025 (estimated)
- Study completion30 Sep 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Cardiac DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 12,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tri-Service General Hospital (TSGH)
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wei-Ting Liu, Tri-Service General Hospital (TSGH)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07038018
- Sponsor's protocol ID
- B202405084
- Status last verified
- Jun 2025
- First submitted
- 16 Jun 2025
- Last updated
- 26 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 35330455
Study at a glance
From the registry record, last updated by the sponsor on 26 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- External Validation of Artificial Intelligence-Enabled Electrocardiograms for the Detection of Reduced Left Ventricular Ejection Fraction
- ClinicalTrials.gov ID
- NCT07038018
- Acronym / sponsor ID
- B202405084
- Conditions
- Cardiac Disease
- Keywords
- left ventricular dysfunction, artificial intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 12,500 participants (estimated)
- Lead sponsor
- Tri-Service General Hospital (TSGH)University, hospital or other
- Study start
- 1 Aug 2025 (estimated)
- Primary completion
- 31 Aug 2025 (estimated)
- Study completion
- 30 Sep 2025 (estimated)
- First posted
- 26 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Data were collected from 13 hospitals, excluding the medical center that developed the artificial intelligence-enabled electrocardiography (AI-ECG) algorithm. The primary objective of the study was to evaluate the sensitivity and specificity of the AI-ECG model in detecting left ventricular dysfunction, defined as left ventricular ejection fraction (LVEF) ≤ 40%. To ensure clinical applicability, predefined thresholds required both sensitivity and specificity to exceed 0.80 in external validation cohorts. Sample size calculations were based on testing the null hypothesis that sensitivity equals 0.80. In the development hospital cohort, the model demonstrated a sensitivity of 0.869 and a specificity of 0.896. With a two-sided significance level (α) of 0.05 and a power of 90%, an estimated 310 cases of LVEF ≤ 40% were required.
Given that the prevalence of left ventricular dysfunction was 4% in the development hospital cohort but expected to be lower-between 2.5% and 3%-in external validation settings (i.e., regional and local hospitals), the total sample size needed to accrue the target number of cases was estimated to range between 10,333 and 12,400 patients. To achieve this, six regional hospitals and seven local hospitals were selected as external validation sites. Because both electrocardiography and echocardiography were required within a seven-day interval-leading to anticipated exclusions-approximately 1,500 patients were targeted from each regional hospital and 500 from each local hospital, resulting in a final target sample size of approximately 12,500 patients.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- All patients with ECGs and an echocardiogram within 7 days
Key inclusion criteria (1 listed)
- patients with ECGs and an echocardiogram within 7 days
Key exclusion criteria (2 listed)
- Missing ECG signals
- Missing LVEF assessment in echocardiograms
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-ECG AlgorithmAI-ECG Algorithm to detect LVEF<=40% | Diagnostic test | – |
Primary outcome
The Sensitivity and specificity of AI-ECG model for left ventricular ejection fraction ≤ 40%
Time frame: within 7 days
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Jun 2025.
- Background: PubMed 35330455Chen 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
13 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiac Disease, run by Tri-Service General Hospital (TSGH) at 13 sites in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 26 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 13 sites in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Wei-Ting Liu, M.D. (+886287923311 ext. 15809, wtliucv@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The Sensitivity and specificity of AI-ECG model for left ventricular ejection fraction ≤ 40% (time frame: within 7 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07038018, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Jun 2025; AzTrials last refreshed its copy on 30 Oct 2026.