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.
Patients aged 19 years or older diagnosed with tachycardias (including PSVT, WPW syndrome, PVC, or VT) who have undergone an electrophysiological study (EPS) and radiofrequency catheter ablation (RFCA).
Patients with 12-lead electrocardiogram (ECG) records extractable as text (XML) files.
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Retrospective cohort of patients diagnosed with tachycardias who underwent electrophysiological studies and catheter ablation at participating institutions\[cite: 1\].
Cohort
Retrospective
AI-based ECG Diagnostic and Localization Algorithm
Diagnostic testApplication 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 Performance (AUC-ROC) of AI-ECG Models
Localization Accuracy of Tachycardia Origin Sites
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