TachycardiaEP-AI ASSISTNCT07859020
AI-Based Differential Diagnosis and Localization of Tachycardia Using ECG: Multicenter Registry (EP-AI ASSIST)
An observational study of Tachycardia and Paroxysmal Supraventricular Tachycardia, 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 19 years and older
- Conditions the study is for
- Tachycardia, Paroxysmal Supraventricular Tachycardia, Wolff-Parkinson-White Syndrome
- What is being tested?
- AI-based ECG Diagnostic and Localization Algorithm
- How many people
- 10,000 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Dec 2023to 1 Dec 2028
- 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 19 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Retrospective cohort of patients aged 19 years or older with PSVT, WPW syndrome, PVC, or VT who underwent electrophysiological study and catheter ablation at participating institutions between January 2005 and December 2023.
Who may be able to join
- Age 19 years or older
- Diagnosis of one of the following arrhythmias: paroxysmal supraventricular tachycardia (PSVT), Wolff-Parkinson-White (WPW) syndrome, premature ventricular contractions (PVC), or ventricular tachycardia (VT)
- Underwent an electrophysiological study (EPS) and catheter ablation
- Available 12-lead electrocardiogram (ECG) records that can be extracted as text (XML) files
Who cannot join
- Inadequate ECG data quality, as judged by the investigator
- Missing medical records required for the study, as judged by the investigator
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: AI-based Differential Diagnosis and Localization of Tachycardia Using ECG: Multicenter Registry (EP-AI ASSIST)
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.
- Conditions
- Tachycardia, Paroxysmal Supraventricular Tachycardia, Wolff-Parkinson-White Syndrome, Premature Ventricular Contractions, Ventricular Tachycardia
- Being tested
- AI-based ECG Diagnostic and Localization Algorithm
- Keywords
- Artificial Intelligence, Electrocardiogram, Transformer, Catheter Ablation, Multicenter Study
- Medical subject headings
- Tachycardia, Tachycardia, Ventricular, Wolff-Parkinson-White Syndrome, Ventricular Premature Complexes
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-based ECG Diagnostic and Localization AlgorithmDiagnostic test
Application of a deep learning-based 12-lead electrocardiogram (ECG) algorithm to analyze raw text data for the differential diagnosis and localization of tachycardias, including PSVT, WPW syndrome, PVC, and VT[cite: 1].
| Study group | Role | What they receive |
|---|---|---|
| Cardiovascular Disease and Tachycardia Cohort | Patients aged 19 years or older diagnosed with tachycardias (including PSVT, WPW syndrome, PVC, or VT) who underwent an electrophysiological study (EPS) and radiofrequency catheter ablation (RFCA), with extractable 12-lead ECG text data[cite: 1]. Receives: Diagnostic Test: AI-based ECG Diagnostic and Localization Algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the ROC Curve (AUC) of AI-ECG Models for Differentiating Tachycardia Mechanisms Confirmed by Electrophysiological Study
Diagnostic performance of Transformer-based deep learning models applied to 12-lead ECG raw data for differentiating tachycardia mechanisms, including atrioventricular nodal reentrant tachycardia (AVNRT) versus atrioventricular reentrant tachycardia (AVRT). The reference standard is the diagnosis confirmed by electrophysiological study and catheter ablation. Performance is summarized as AUC with 95% confidence interval (range 0-1; higher values indicate better discrimination).
Measured: Baseline
Secondary outcomes (1)
Area Under the ROC Curve (AUC) of AI-ECG Models for Localizing Arrhythmia Origin Sites (Accessory Pathway, PVC, and VT) Confirmed by Successful Catheter Ablation
Performance of AI-ECG models in classifying the anatomical site of the arrhythmia substrate from the pre-ablation 12-lead ECG, including (1) accessory pathway location in Wolff-Parkinson-White syndrome, (2) the origin of premature ventricular contractions, and (3) the origin of ventricular tachycardia. The reference standard is the site identified by electrophysiological mapping and confirmed by successful catheter ablation. For each classification task, performance is reported as AUC with 95% confidence interval (range 0-1; higher values indicate better classification).
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Oct 2026
- Study start1 Dec 2023
- Primary completion1 Dec 2028 (estimated)
- Study completion1 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for TachycardiaStudies 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
- 10,000 participants (estimated)
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
- NCT07859020
- Sponsor's protocol ID
- 2023-12-033
- Other IDs
- 2023-12-033
- Acronym
- EP-AI ASSIST
- Status last verified
- Oct 2026
- First submitted
- 29 Sep 2026
- Last updated
- 30 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data (IPD) will not be shared with other researchers due to institutional data privacy policies and ethical regulations concerning retrospective clinical records and encrypted electrocardiogram data.
Study at a glance
From the registry record, last updated by the sponsor on 30 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-based Differential Diagnosis and Localization of Tachycardia Using ECG: Multicenter Registry (EP-AI ASSIST)
- ClinicalTrials.gov ID
- NCT07859020
- Acronym / sponsor ID
- EP-AI ASSIST / 2023-12-033
- Conditions
- Tachycardia, Paroxysmal Supraventricular Tachycardia, Wolff-Parkinson-White Syndrome, Premature Ventricular Contractions, Ventricular Tachycardia
- Keywords
- Artificial Intelligence, Electrocardiogram, Transformer, Catheter Ablation, Multicenter Study
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Inha University HospitalUniversity, hospital or other
- Study start
- 1 Dec 2023 (actual)
- Primary completion
- 1 Dec 2028 (estimated)
- Study completion
- 1 Dec 2028 (estimated)
- First posted
- 5 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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Cardiovascular Disease and Tachycardia Cohort | – | Diagnostic Test: AI-based ECG Diagnostic and Localization AlgorithmPatients aged 19 years or older diagnosed with tachycardias (including PSVT, WPW syndrome, PVC, or VT) who underwent an electrophysiological study (EPS) and radiofrequency catheter ablation (RFCA), with extractable 12-lead ECG text data[cite: 1]. |
Who may qualify
- Ages
- 19 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Retrospective cohort of patients aged 19 years or older with PSVT, WPW syndrome, PVC, or VT who underwent electrophysiological study and catheter ablation at participating institutions between January 2005 and December 2023.
Key inclusion criteria (4 listed)
- Age 19 years or older
- Diagnosis of one of the following arrhythmias: paroxysmal supraventricular tachycardia (PSVT), Wolff-Parkinson-White (WPW) syndrome, premature ventricular contractions (PVC), or ventricular tachycardia (VT)
- Underwent an electrophysiological study (EPS) and catheter ablation
- Available 12-lead electrocardiogram (ECG) records that can be extracted as text (XML) files
Key exclusion criteria (2 listed)
- Inadequate ECG data quality, as judged by the investigator
- Missing medical records required for the study, as judged by the investigator
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-based ECG Diagnostic and Localization AlgorithmApplication of a deep learning-based 12-lead electrocardiogram (ECG) algorithm to analyze raw text data for the differential diagnosis and localization of tachycardias, including PSVT, WPW syndrome, PVC, and VT[cite: 1]. | Diagnostic test | Cardiovascular Disease and Tachycardia Cohort |
Primary outcome
Area Under the ROC Curve (AUC) of AI-ECG Models for Differentiating Tachycardia Mechanisms Confirmed by Electrophysiological Study
Time frame: Baseline
Diagnostic performance of Transformer-based deep learning models applied to 12-lead ECG raw data for differentiating tachycardia mechanisms, including atrioventricular nodal reentrant tachycardia (AVNRT) versus atrioventricular reentrant tachycardia (AVRT). The reference standard is the diagnosis confirmed by electrophysiological study and catheter ablation. Performance is summarized as AUC with 95% confidence interval (range 0-1; higher values indicate better discrimination).
1 secondary outcome
Area Under the ROC Curve (AUC) of AI-ECG Models for Localizing Arrhythmia Origin Sites (Accessory Pathway, PVC, and VT) Confirmed by Successful Catheter Ablation
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 30 Oct 2026.
- Individual participant dataSharing planned: No. Individual participant data (IPD) will not be shared with other researchers due to institutional data privacy policies and ethical regulations concerning retrospective clinical records and encrypted electrocardiogram data.
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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 Tachycardia and Paroxysmal Supraventricular Tachycardia, 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 4 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: looking for participants now, as of the sponsor's last update on 30 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?
Area Under the ROC Curve (AUC) of AI-ECG Models for Differentiating Tachycardia Mechanisms Confirmed by Electrophysiological Study (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 30 Oct 2026.