A Combined Deep Learning-Regression Paradigm for Echocardiography-Based Left Ventricle Ejection Fraction Prediction

One of the most widely used imaging modalities for evaluating the structure and function of the heart is echocardiography. Many features related to the heart's four chambers are driven by echocardiography to diagnose heart health. The left ventricle ejection fraction (LVEF) is one of the most influential characteristics considering the left ventricle. This study uses echocardiographic videos from the EchoNet-Dynamic dataset to evaluate left ventricular function, focusing on apical four-chamber (A4C) views. Sequential frame extraction from these videos enabled the application of novel methods yielding precise results. Extracted features were employed in the Gaussian Process Regression (GPR) model to predict LVEF, demonstrating superior performance (Root Mean Square Error (RMSE)= 3.66, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>= 0.957) compared to other methods explored. These findings underscore the potential of our proposed methodology in enhancing cardiac function assessment through echocardiographic analysis.