Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up

What this Trial Is About

Researchers are evaluating the diagnostic power of artificial intelligence-enhanced electrocardiography (AI-ECG) to detect multi-system diseases. This observational study collects data from inpatient, emergency, and outpatient populations to create ECG-based models for diagnosis, screening, and prediction across various systemic conditions including cardiovascular, digestive, respiratory, nervous, endocrine, metabolic, urogenital, hematologic, neoplastic, and mental health disorders. It also aims to predict new disease onset and one-year mortality risk. Participants undergo an ECG screening as part of routine care, with data used to develop AI models that analyze ECG patterns for signs of multiple diseases. The study leverages ECG's portability, cost-effectiveness, and non-invasive nature to enable rapid screening in diverse medical settings. The approach also focuses on understanding how ECG abnormalities relate to different disease mechanisms and improving AI interpretability. During participation, patients provide ECG data along with clinical diagnosis information, which researchers use to refine and validate AI models. The main outcome measured is the ability to predict multi-system diseases based on ECG within one month. The study involves continuous monitoring of ECG results and clinical diagnoses, with no experimental treatment administered. Participation duration varies based on clinical care timing and data collection.

Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up