AI-Powered Electrocardiography for Diagnosis and Prognostic Prediction in Cardiovascular Disease (AI-CVD): Multicenter Retrospective Study
Condition(s) studied
Investigational drug(s) / intervention(s)
Retrospective ECG Data Analysis: Analysis of de-identified 12-lead electrocardiogram raw data using deep learning models for cardiovascular disease diagnosis and prognosis.
Study 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.
Eligibility
Primary outcome measure(s)
- Diagnostic Performance (AUC-ROC) of AI-ECG Models — 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.
Trial sites (1)
| Facility | City | Region | Status |
|---|---|---|---|
| Inha University Hospital | Incheon | Incheon | Recruiting |
More Inha University Hospital trials in South Korea
Other trials for the same condition
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
View NCT07856472 on ClinicalTrials.gov ↗ ← All trials in South Korea