Ventricular Hypertrophy

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.

The goal of this clinical trial is to compare the time required to record a complete Electrocardiogram (ECG) trace using the DigitalCardio (CardioSecur®) tablet device versus a standard 12-lead ECG device in eligible patients. The main questions it aims to answer are: Is there a difference in diagnostic recording time (in minutes) between the DigitalCardio tablet and a standard ECG machine? How do the two devices compare in terms of trace quality (including QRS duration, QT interval, frontal axis measurements, and missing/erroneous leads)? How satisfied are healthcare professionals with the CardioSecur® device? Researchers will compare Group A (standard 10-electrode ECG followed by 4-electrode ECG) to Group B (4-electrode ECG followed by standard 10-electrode ECG) in a 1:1 randomized crossover design to evaluate recording efficiency and trace quality. Participants will: Complete an initial screening visit with the principal investigator to review eligibility criteria and sign the informed consent form. Be randomly assigned (1:1 ratio) to either Group A or Group B. Undergo both ECG recording methods sequentially (a standard 10-electrode ECG and a 4-electrode ECG using the DigitalCardio tablet), while researchers measure the total time elapsed from electrode placement to obtaining a complete trace.