Abstract

Myotonic dystrophies (DM) are neuromuscular conditions that cause widespread effects throughout the body. There are brain white matter changes on MRI in patients with DM that correlate with neuropsychological functional changes. How these brain alterations causally relate to the presence and severity of cognitive symptoms remains largely unknown. Deep neural networks have significantly improved the performance of image classification of huge datasets. However, its application in brain imaging is limited and not well described, due to the scarcity of labeled training data. In this work, we propose an approach for the diagnosis of DM based on a spatio-temporal deep learning paradigm. The obtained accuracy (73.71%) and sensitivities and specificities showed that the implemented approach based on 4-D convolutional neural networks leads to a compact, discriminative, and fast computing DM-based clinical medical decision support system.Clinical relevance- Many adults with DM experience cognitive and neurological effects impacting their quality of life, and ability to maintain employment. A robust and reliable DM-based clinical decision support system may help reduce the long diagnostic delay common to DM. Furthermore, it can help neurologists better understand the pathophysiology of the disease and analyze effects of new drugs that aim to address the neurological symptoms of DM.

View details for DOI 10.1109/EMBC44109.2020.9176455

View details for PubMedID 33018327