  Hyper-trabeculation or non-compaction in the left ventricle of the myocardium
(LVNC) is a recently classified form of cardiomyopathy. Several methods have
been proposed to quantify the trabeculae accurately in the left ventricle, but
there is no general agreement in the medical community to use a particular
approach. In previous work, we proposed DL-LVTQ, a deep learning approach for
left ventricular trabecular quantification based on a U-Net CNN architecture.
DL-LVTQ was an automatic diagnosis tool developed from a dataset of patients
with the same cardiomyopathy (hypertrophic cardiomyopathy).
  In this work, we have extended and adapted DL-LVTQ to cope with patients with
different cardiomyopathies. The dataset consists of up 379 patients in three
groups with different particularities and cardiomyopathies. Patient images were
taken from different scanners and hospitals. We have modified and adapted the
U-Net convolutional neural network to account for the different particularities
of a heterogeneous group of patients with various unclassifiable or mixed and
inherited cardiomyopathies.
  The inclusion of new groups of patients has increased the accuracy,
specificity and kappa values while maintaining the sensitivity of the automatic
deep learning method proposed. Therefore, a better-prepared diagnosis tool is
ready for various cardiomyopathies with different characteristics.
Cardiologists have considered that 98.9% of the evaluated outputs are verified
clinically for diagnosis. Therefore, the high precision to segment the
different cardiac structures allows us to make a robust diagnostic system
objective and faster, decreasing human error and time spent.
