  Delayed-enhancement cardiac magnetic resonance (DE-CMR)provides important
diagnostic and prognostic information on myocardial viability. The presence and
extent of late gadolinium enhancement (LGE)in DE-CMR is negatively associated
with the probability of improvement in left ventricular function after
revascularization. Moreover, LGE findings can support the diagnosis of several
other cardiomyopathies, but their absence does not rule them out, making
disease classification by visual assessment difficult. In this work, we propose
deep learning neural networks that can automatically predict myocardial disease
from patient clinical information and DE-CMR. All the proposed networks achieve
very good classification accuracy (>85%). Including information from DE-CMR
(directly as images or as metadata following DE-CMR segmentation) is valuable
in this classification task, improving the accuracy to 95-100%.
