Cardiovascular DiseasesAI-CVDNCT07856472
AI-Powered Electrocardiography for Diagnosis and Prognostic Prediction in Cardiovascular Disease (AI-CVD): Multicenter Retrospective Study
An observational study of Cardiovascular Diseases and Coronary Artery Disease, run by Inha University Hospital at 1 site in South Korea.
Trial summary
This multicenter retrospective study aims to develop, validate, and clinically apply an artificial intelligence (AI)-powered electrocardiography (ECG) algorithm for the diagnosis and prognostic prediction of cardiovascular diseases, including coronary artery disease and heart failure. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cardiovascular Diseases, Coronary Artery Disease, Heart Failure
- What is being tested?
- Retrospective ECG Data Analysis
- How many people
- 10,000 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Mar 2023to 1 Mar 2028
- How long will I take part?
- 5 Years
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients visiting the participating tertiary hospitals with extractable electrocardiogram records and diagnosed with cardiovascular diseases.
Who may be able to join
- Patients aged 18 years or older diagnosed with cardiovascular diseases, including coronary artery disease and heart failure.
- Patients with 12-lead electrocardiogram (ECG) records extractable as text (XML
Who cannot join
- Patients deemed inappropriate as study subjects by the investigator's judgment
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
South Korea1 site, 1 recruiting
- Recruiting
Inha University Hospital
Incheon, Incheon, 22332
Yongsoo Baek, MD, PhD, +82-32-890-2200, existsoo@inha.ac.kr
About this study
Official title: Research for the Development and Clinical Application of Artificial Intelligence-powered Electrocardiography for Diagnosis and Prognostic Prediction in Cardiovascular Disease (AI-CVD): Multicenter Retrospective Study
This multicenter retrospective study aims to develop, validate, and clinically apply an artificial intelligence (AI)-powered electrocardiography (ECG) algorithm for the diagnosis and prognostic prediction of cardiovascular diseases, including coronary artery disease and heart failure. By utilizing encrypted raw 12-lead ECG text data and deep neural network models, the study evaluates the effectiveness of AI-enhanced ECG in detecting cardiac abnormalities and predicting clinical outcomes.
Read the detailed description
Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality. Standard 12-lead ECGs are widely accessible tools for cardiovascular screening and diagnosis; however, traditional visual interpretations have limitations. Recent advancements in deep learning offer unprecedented performance in extracting hidden patterns from ECG signals.
This study is a retrospective, multicenter investigation conducted across multiple tertiary medical centers in South Korea. The primary objectives are:
- To extract and encrypt raw 12-lead ECG data (XML format) into binary datasets to ensure patient privacy and data standardization.
- To advance and validate deep neural network models-specifically combining Bi-LSTM and attention mechanisms-for diagnosing conditions such as coronary artery disease, heart failure, and related structural heart diseases.
- To assess the clinical utility and prognostic performance of AI-ECG risk prediction models in real-world cohorts spanning data from 2005 to 2022.
- Conditions
- Cardiovascular Diseases, Coronary Artery Disease, Heart Failure, Atrial Fibrillation (AF), Ventricular Hypertrophy
- Being tested
- Retrospective ECG Data Analysis
- Keywords
- Artificial Intelligence, Electrocardiogram, Deep Learning, Multicenter Study, Retrospective Study
- Medical subject headings
- Cardiovascular Diseases, Coronary Artery Disease, Heart Failure, Atrial Fibrillation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Retrospective ECG Data AnalysisOther
Analysis of de-identified 12-lead electrocardiogram raw data using deep learning models for cardiovascular disease diagnosis and prognosis.
| Study group | Role | What they receive |
|---|---|---|
| Cardiovascular Disease Cohort | Patients aged 18 years or older diagnosed with cardiovascular diseases, including coronary artery disease and heart failure, who have extractable text-format (XML) 12-lead electrocardiogram records. Receives: Other: Retrospective ECG Data Analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Performance (AUC-ROC) of AI-ECG Models
To evaluate the area under the receiver operating characteristic curve (AUC-ROC) of the deep neural network-based 12-lead ECG model for diagnosing cardiovascular diseases compared to reference clinical standards.
Measured: Baseline
Secondary outcomes (1)
Sensitivity and Specificity of AI-ECG Models
To evaluate the association between AI-ECG risk scores and long-term clinical outcomes, including all-cause mortality and heart failure-related hospitalizations.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Oct 2026
- Study start1 Mar 2023
- Primary completion1 Mar 2028 (estimated)
- Study completion1 Mar 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiovascular DiseasesStudies 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
- Other
- Enrolment
- 10,000 participants (estimated)
- Target duration
- 5 Years
Sponsor and investigators
- Lead sponsor
- Inha University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yong-Soo Baek, Inha University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07856472
- Sponsor's protocol ID
- 2023-03-024
- Other IDs
- 2023-03-024
- Acronym
- AI-CVD
- Status last verified
- Sep 2026
- First submitted
- 28 Sep 2026
- Last updated
- 2 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Data are not available due to institutional policy and patient privacy regulations regarding retrospective clinical records.
Study at a glance
From the registry record, last updated by the sponsor on 2 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research for the Development and Clinical Application of Artificial Intelligence-powered Electrocardiography for Diagnosis and Prognostic Prediction in Cardiovascular Disease (AI-CVD): Multicenter Retrospective Study
- ClinicalTrials.gov ID
- NCT07856472
- Acronym / sponsor ID
- AI-CVD / 2023-03-024
- Conditions
- Cardiovascular Diseases, Coronary Artery Disease, Heart Failure, Atrial Fibrillation (AF), Ventricular Hypertrophy
- Keywords
- Artificial Intelligence, Electrocardiogram, Deep Learning, Multicenter Study, Retrospective Study
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Inha University HospitalUniversity, hospital or other
- Study start
- 1 Mar 2023 (actual)
- Primary completion
- 1 Mar 2028 (estimated)
- Study completion
- 1 Mar 2028 (estimated)
- First posted
- 2 Oct 2026 (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 multicenter retrospective study aims to develop, validate, and clinically apply an artificial intelligence (AI)-powered electrocardiography (ECG) algorithm for the diagnosis and prognostic prediction of cardiovascular diseases, including coronary artery disease and heart failure. By utilizing encrypted raw 12-lead ECG text data and deep neural network models, the study evaluates the effectiveness of AI-enhanced ECG in detecting cardiac abnormalities and predicting clinical outcomes.
Detailed description
Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality. Standard 12-lead ECGs are widely accessible tools for cardiovascular screening and diagnosis; however, traditional visual interpretations have limitations. Recent advancements in deep learning offer unprecedented performance in extracting hidden patterns from ECG signals.
This study is a retrospective, multicenter investigation conducted across multiple tertiary medical centers in South Korea. The primary objectives are:
- To extract and encrypt raw 12-lead ECG data (XML format) into binary datasets to ensure patient privacy and data standardization.
- To advance and validate deep neural network models-specifically combining Bi-LSTM and attention mechanisms-for diagnosing conditions such as coronary artery disease, heart failure, and related structural heart diseases.
- To assess the clinical utility and prognostic performance of AI-ECG risk prediction models in real-world cohorts spanning data from 2005 to 2022.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 5 Years
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Cardiovascular Disease Cohort | – | Other: Retrospective ECG Data AnalysisPatients aged 18 years or older diagnosed with cardiovascular diseases, including coronary artery disease and heart failure, who have extractable text-format (XML) 12-lead electrocardiogram records. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients visiting the participating tertiary hospitals with extractable electrocardiogram records and diagnosed with cardiovascular diseases.
Key inclusion criteria (2 listed)
- Patients aged 18 years or older diagnosed with cardiovascular diseases, including coronary artery disease and heart failure.
- Patients with 12-lead electrocardiogram (ECG) records extractable as text (XML
Key exclusion criteria (1 listed)
- Patients deemed inappropriate as study subjects by the investigator's judgment
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 |
|---|---|---|
| Retrospective ECG Data AnalysisAnalysis of de-identified 12-lead electrocardiogram raw data using deep learning models for cardiovascular disease diagnosis and prognosis. | Other | Cardiovascular Disease Cohort |
Primary outcome
Diagnostic Performance (AUC-ROC) of AI-ECG Models
Time frame: Baseline
To evaluate the area under the receiver operating characteristic curve (AUC-ROC) of the deep neural network-based 12-lead ECG model for diagnosing cardiovascular diseases compared to reference clinical standards.
1 secondary outcome
Sensitivity and Specificity of AI-ECG Models
Time frame: Baseline
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 2 Oct 2026.
- Individual participant dataSharing planned: No. Data are not available due to institutional policy and patient privacy regulations regarding retrospective clinical records.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiovascular Diseases and Coronary Artery Disease, run by Inha University Hospital at 1 site in South Korea.
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 2 inclusion and 1 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: looking for participants now, as of the sponsor's last update on 2 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. 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 Yongsoo Baek, MD, PhD (+82-32-890-2200, existsoo@inha.ac.kr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Performance (AUC-ROC) of AI-ECG Models (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 2 Oct 2026.