Artificial intelligence-enabled reconstruction of the right ventricular pressure curve using the peak pressure value: a proof-of-concept study.

Abstract

Aims: Conventional echocardiographic parameters of right ventricular (RV) function are afterload-dependent. Therefore, incorporating RV pressures may enable the formulation of new parameters that reflect intrinsic RV function accurately. Accordingly, we sought to develop an artificial intelligence-based method to reconstruct the RV pressure curve based on the peak RV pressure.

Methods and results: We invasively acquired RV pressure in 29 heart failure patients before and after implanting a left ventricular (LV) assist device. Using these tracings, we trained various machine learning models to reconstruct the RV pressure curve of the entire cardiac cycle based on the peak value of the curve. The best-performing model was compared with two other methods that estimated RV pressures based on a reference LV and RV pressure curve, respectively. Seventeen consecutive patients from another centre who underwent right heart catheterization and simultaneous echocardiography served as an external validation cohort. Among the evaluated algorithms, multilayer perceptron (MLP) achieved the best performance with an R ² of 0.887 (0.834-0.941). The RV and LV reference curve-based methods achieved R ² values of 0.879 (0.815-0.943) and 0.636 (0.500-0.771), respectively. During external validation, MLP exhibited similarly good performance [R ² 0.911 (0.873-0.948)], which decreased only modestly if the echocardiography-derived peak RV pressure was used instead of the invasively measured peak RV pressure [R ² 0.802 (0.694-0.909)].

Conclusions: The proposed method enables the reconstruction of the RV pressure curve using only the peak value as input. Thus, it may serve as a fundamental component for developing new echocardiographic tools targeting the afterload-adjusted assessment of RV function.