AI-Powered Electrocardiography for Diagnosis and Prognostic Prediction in Cardiovascular Disease (AI-CVD): Multicenter Retrospective Study

NCT07856472 | Recruiting

• 1 other identifier: 2023-03-024
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief 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. 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

Cardiovascular Diseases; Coronary Artery Disease; Heart Failure; Atrial Fibrillation (AF); Ventricular Hypertrophy

Keywords

Artificial Intelligence; Electrocardiogram; Deep Learning; Multicenter Study; Retrospective Study

Outcome Measures

Primary Outcomes (1)

• 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.

  Baseline

Secondary Outcomes (1)

• Sensitivity and Specificity of AI-ECG Models

  Baseline

Study Arms (1)

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.

Other: Retrospective ECG Data Analysis

Interventions

Retrospective ECG Data Analysis OTHER

Analysis of de-identified 12-lead electrocardiogram raw data using deep learning models for cardiovascular disease diagnosis and prognosis.

Cardiovascular Disease Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients visiting the participating tertiary hospitals with extractable electrocardiogram records and diagnosed with cardiovascular diseases.

You may qualify if:

• 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

You may not qualify if:

• \- Patients deemed inappropriate as study subjects by the investigator's judgment

Sponsors & Collaborators

• Inha University Hospital: lead

Study Sites (1)

Inha University Hospital

Incheon, Incheon, 22332, South Korea

RECRUITING

MeSH Terms

Conditions

Cardiovascular Diseases; Coronary Artery Disease; Heart Failure; Atrial Fibrillation

Condition Hierarchy (Ancestors)

Coronary Disease; Myocardial Ischemia; Heart Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Vascular Diseases; Arrhythmias, Cardiac; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Yongsoo Baek, MD, PhD

CONTACT

+82-32-890-2200; existsoo@inha.ac.kr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: September 28, 2026
• First Posted: October 2, 2026
• Study Start: March 1, 2023
• Primary Completion (Estimated): March 1, 2028
• Study Completion (Estimated): March 1, 2028
• Last Updated: October 2, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)