Keywords: Myocardium, Cardiovascular, ischemia, deep-learning, strains, remodelling, function
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.
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