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Soc. Open Sci."],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Multiple sclerosis (MS) is an autoimmune disease of the brain and spinal cord with both inflammatory and neurodegenerative features. Although advances in imaging techniques, particularly magnetic resonance imaging (MRI), have improved the process of diagnosis, its cause is unknown, a cure remains elusive and the evidence base to guide treatment is lacking. Computational techniques like machine learning (ML) have started to be used to understand MS. Published MS MRI-based computational studies can be divided into five categories: automated diagnosis; differentiation between lesion types and\/or MS stages; differential diagnosis; monitoring and predicting disease progression; and synthetic MRI dataset generation. Collectively, these approaches show promise in assisting with MS diagnosis, monitoring of disease activity and prediction of future progression, all potentially contributing to disease management. 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