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

Summary

This prospective observational study (ECHO-FIT Study) aims to develop and validate a predictive model for cardiac function, particularly left ventricular ejection fraction (LVEF), by integrating echocardiographic measurements with body composition data obtained from the QCCUNIQ BC 720 device. The study plans to enroll 2,000 adult participants, comprising 1,000 individuals with normal LVEF (≥50%) and 1,000 with heart failure (LVEF \<50%), all of whom will undergo standard-of-care echocardiography and body composition analysis. By analyzing the relationships between key echocardiographic parameters (such as LVEF and diastolic function) and body composition measures (including fat mass, skeletal muscle mass, and total body water), we will develop a non-invasive prediction model capable of identifying individuals at higher risk of cardiac dysfunction. This innovative approach has the potential to enhance early detection and personalized management of heart failure, reduce dependence on resource-intensive diagnostic procedures, and ultimately improve patient outcomes.

Eligibility

Inclusion Criteria:

* Aged 20 years or older.
* Undergoing a standard echocardiographic examination.
* Providing consent to undergo body composition analysis.
* Signing the informed consent form to voluntarily participate in the study.

Exclusion Criteria:

* Having a physical or mental condition that makes it impossible to conduct an echocardiogram or perform body composition analysis.
* Deemed inappropriate for study participation by the researcher (e.g., unable to cooperate).

Conditions5

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