Multiple sclerosis (MS) is characterized by inflammatory activity and neurodegeneration, leading to the accumulation of damage to the central nervous system resulting in the accumulation of disability. MRI depicts an important part of the pathology of this disease and therefore plays a key part in diagnosis and disease monitoring. Still, major challenges exist with regard to the differential diagnosis, adequate monitoring of disease progression, quantification of CNS damage, and prediction of disease progression. Machine learning techniques have been employed in an attempt to overcome these challenges. This chapter aims to give an overview of how machine learning techniques are employed in MS with applications for diagnostic classification, lesion segmentation, improved visualization of relevant brain pathology, characterization of neurodegeneration, and prognostic subtyping.
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