An observational study in Cardiovascular Diseases, Coronary Artery Disease and Heart Failure, sponsored by Inha University Hospital. Recruiting at 1 site in South Korea. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-10-02.
Sponsored by Inha University Hospital · Observational
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.
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:
4,904 studies on the registry are indexed under Cardiovascular Diseases; 919 are open to participants now.
This study's planned enrollment of 10,000 is above the median of 573 across 1,485 observational studies indexed under Cardiovascular Diseases.
Browse Cardiovascular Diseases studies →Inha University Hospital is the lead sponsor of 37 studies on the registry; 8 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Patients visiting the participating tertiary hospitals with extractable electrocardiogram records and diagnosed with cardiovascular diseases.
Exclusion Criteria:
- Patients deemed inappropriate as study subjects by the investigator's judgment
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
Analysis of de-identified 12-lead electrocardiogram raw data using deep learning models for cardiovascular disease diagnosis and prognosis.
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.
Time frame: Baseline
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.
Time frame: Baseline
Plan to share: No — Data are not available due to institutional policy and patient privacy regulations regarding retrospective clinical records.
No publications or documents are linked to this record.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
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Inha University Hospital