Study of AI Prediction for Heart Function Using Echocardiography and Body Composition Analysis

What this Trial Is About

This research aims to develop and validate a model that predicts heart function, especially the left ventricular ejection fraction LVEF, by combining echocardiography results with body composition data from the QCCUNIQ BC 720 device. The study focuses on adults with and without heart failure to better understand how body composition relates to heart health and to enhance early detection of cardiac dysfunction. Participants will undergo standard echocardiographic exams to measure heart function and body composition analysis using the QCCUNIQ BC 720 device within one week of their heart scan. The study includes 2,000 adults split evenly between those with normal heart function LVEF 6550% and those with heart failure LVEF below 50%. Researchers will analyze these data using traditional and advanced machine learning methods to create a predictive model. Throughout the study, participants will have their heart function and body composition assessed, with data collected on parameters like fat mass, muscle mass, and total body water. The study will track outcomes such as LVEF within one week and evaluate the models accuracy through statistical validation. The goal is to find a non-invasive method to identify individuals at risk for heart problems, potentially reducing reliance on complex diagnostics and improving personalized care.

AI-based Prediction of Cardiac Function Using Echocardiography and Body Composition Data (ECHO-FIT Study)