Overview
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.
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.
Eligibility
Inclusion Criteria:
- 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
Exclusion Criteria:
\- Patients deemed inappropriate as study subjects by the investigator's judgment
