Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension.

Background: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in right ventricular (RV) afterload, impairing systolic function. Two-dimensional (2D) echocardiography is the most performed cardiac imaging tool to assess RV systolic function; however, an accurate evaluation requires expertise. We aimed to develop a fully automated deep learning (DL)-based tool to estimate the RV ejection fraction (RVEF) from 2D echocardiographic videos of apical four-chamber views in patients with precapillary PH.

Echocardiographic estimation of right ventricular diastolic stiffness based on pulmonary regurgitant velocity waveform analysis in precapillary pulmonary hypertension.

Right ventricular (RV) diastolic stiffness is an independent predictor of survival and is strongly associated with disease severity in patients with precapillary pulmonary hypertension (PH). Therefore, a fully validated echocardiographic method for assessing RV diastolic stiffness needs to be established. This study aimed to compare echocardiography-derived RV diastolic stiffness and invasively measured pressure-volume loop-derived RV diastolic stiffness in patients with precapillary PH.