A deep learning framework for assessing remodelling and functional changes in infarcted left ventricles with reduced ejection fraction

Motivation: To perform a retrospective analysis of the anatomical and functional remodelling of left ventricles 4 and 12 months after the ischemic event. Goal(s): To assess differences in remodelling between patients with recovered function and those without. Approach: A deep-learning framework was developed to fit a statistical shape model to all cardiac phases of each patient and compute strains, valve motion and morphological descriptors. Results: Peak strain values and valve displacements at 4 and 12 months show different trends between patients with recovered function and those without. Peak-systolic shapes of patients with positive remodelling show a lower sphericity with respect to the others. Impact: A deep learning framework reveals that relative changes in peak systolic anatomical shapes, radial and circumferential strains and valve motion after 4 months could provide a discriminator for predicting positive remodelling and restoration of functionality in patient with heart failure.