Figure 4:

Illustration of how features of RV motion are automatically selected for prognostic importance in patients with PH. A, Plot represents how the magnitude of systolic excursion in the right ventricle, derived from atlas-based cardiac segmentations, varies between survivors and nonsurvivors from the basal level to the apical level. B, Plot shows where supervised machine learning identifies features within these motion-based data that most accurately allow discrimination between low-risk and high-risk patients. The full model used for survival prediction took into account the prognostic importance of motion throughout a 3D representation of the right ventricle, resolved into orthogonal components.