Classification of MS patients into disability levels using deep learning approaches based solely on routinely-acquired MRI
Classification of MS patients into disability levels using deep learning approaches based solely on routinely-acquired MRI
L. Coll,P. Carbonell-Mirabent,21 Authors,C. Tur
TLDR
Assessment of the ability of Convolutional Neural Networks to classify relapse-onset MS patients into non-disabled and markedly disabled using only MRI images showed that the CNN models performed better, having the intrinsic potential to improve after the inclusion of regional priors and other valuable clinical data.
Abstract
The ability of existing MRI biomarkers to predict MS patients’ prognosis is limited and inaccurate to be used at the individual level. We aimed to assess the ability of Convolutional Neural Networks (CNNs) to classify relapse-onset MS patients into non-disabled and markedly disabled using only MRI images. T1w and T2w-FLAIR images of 538 MS patients were used to train and test two CNN approaches which were compared also with (conventional) logistic regression models. The results showed that the CNN models performed better, having the intrinsic potential to improve after the inclusion of regional priors and other valuable clinical data.