Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.
Szekely-Kohn, Adam C Castellani, Marco Espino, Daniel M Baronti, Luca Ahmed, Zubair Manifold, William G K Douglas, Michael
Szekely-Kohn, Adam C
Castellani, Marco
Espino, Daniel M
Baronti, Luca
Ahmed, Zubair
Manifold, William G K
Douglas, Michael
Abstract
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. Analysis quality using ML is highly dependent on the dataset size and variability used for training. Wider public access would mean larger datasets for experimentation, resulting in higher-quality analysis, permitting for more conclusive research. This narrative review provides an outline of the fundamentals of MS pathology and pathogenesis, diagnostic techniques and data types in computational analysis, as well as collating literature pertaining to the application of computational techniques to MRI towards developing a better understanding of MS. Competing Interests: We declare we have no competing interests. � 2025 The Author(s).
MIDER Authors
Citations
Altmetric:
Date
2025-01-22
Type
Subject
Radiology
Citation
Szekely-Kohn AC, Castellani M, Espino DM, Baronti L, Ahmed Z, Manifold WGK, Douglas M. Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review. R Soc Open Sci. 2025 Jan 22;12(1):241052. doi: 10.1098/rsos.241052
Journal / Source Title
DOI
10.1098/rsos.241052
PMID
39845718
Publisher
Royal Society Publishing