Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review
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 el...
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| Format: | Article |
| Language: | English |
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The Royal Society
2025-01-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.241052 |
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| author | Adam C. Szekely-Kohn Marco Castellani Daniel M. Espino Luca Baronti Zubair Ahmed William G. K. Manifold Michael Douglas |
| author_facet | Adam C. Szekely-Kohn Marco Castellani Daniel M. Espino Luca Baronti Zubair Ahmed William G. K. Manifold Michael Douglas |
| author_sort | Adam C. Szekely-Kohn |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-826b72e2c4634a159b0aec650dad70f8 |
| institution | Kabale University |
| issn | 2054-5703 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-826b72e2c4634a159b0aec650dad70f82025-08-20T03:52:48ZengThe Royal SocietyRoyal Society Open Science2054-57032025-01-0112110.1098/rsos.241052Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative reviewAdam C. Szekely-Kohn0Marco Castellani1Daniel M. Espino2Luca Baronti3Zubair Ahmed4William G. K. Manifold5Michael Douglas6School of Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UKSchool of Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UKSchool of Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UKSchool of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, UKUniversity Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham B15 2GW, UKThe Royal London Hospital, Barts Health NHS Trust, Whitechapel Road, London E1 1FR, UKUniversity Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham B15 2GW, UKMultiple 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.https://royalsocietypublishing.org/doi/10.1098/rsos.241052artificial intelligencecomputational methodsmachine learningmagnetic resonance imaging (MRI)multiple sclerosis (MS) |
| spellingShingle | Adam C. Szekely-Kohn Marco Castellani Daniel M. Espino Luca Baronti Zubair Ahmed William G. K. Manifold Michael Douglas Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review Royal Society Open Science artificial intelligence computational methods machine learning magnetic resonance imaging (MRI) multiple sclerosis (MS) |
| title | Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review |
| title_full | Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review |
| title_fullStr | Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review |
| title_full_unstemmed | Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review |
| title_short | Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review |
| title_sort | machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis a narrative review |
| topic | artificial intelligence computational methods machine learning magnetic resonance imaging (MRI) multiple sclerosis (MS) |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.241052 |
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