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Review
. 2021 Aug 11:12:700582.
doi: 10.3389/fimmu.2021.700582. eCollection 2021.

Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images

Faezeh Moazami  1 , Alain Lefevre-Utile  1   2   3 , Costas Papaloukas  4 , Vassili Soumelis  1   5
Affiliations
Review

Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images

Faezeh Moazami et al. Front Immunol. .

Abstract

Multiple sclerosis (MS) is one of the most common autoimmune diseases which is commonly diagnosed and monitored using magnetic resonance imaging (MRI) with a combination of clinical manifestations. The purpose of this review is to highlight the main applications of Machine Learning (ML) models and their performance in the MS field using MRI. We reviewed the articles of the last decade and grouped them based on the applications of ML in MS using MRI data into four categories: 1) Automated diagnosis of MS, 2) Prediction of MS disease progression, 3) Differentiation of MS stages, 4) Differentiation of MS from similar disorders.

Keywords: artificial intelligence; disability prediction; machine learning; magnetic resonance imaging (MRI); multiple sclerosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart showing the process of using ML Learning to study MS through MRI images.
See this image and copyright information in PMC

References

    1. Browne P, Chandraratna D, Angood C, Tremlett H, Baker C, Taylor BV, et al. . Atlas of Multiple Sclerosis 2013: A Growing Global Problem With Widespread Inequity. Neurology (2014) 83(11):1022–4. 10.1212/WNL.0000000000000768 - DOI - PMC - PubMed
    1. Wallin MT, Culpepper WJ, Campbell JD, Nelson LM, Langer-Gould A, Marrie RA, et al. . The Prevalence of MS in the United States: A Population-Based Estimate Using Health Claims Data. Neurology (2019) 92(10):e1029–40. 10.1212/WNL.0000000000007035 - DOI - PMC - PubMed
    1. Thompson AJ, Baranzini SE, Geurts J, Hemmer B, Ciccarelli O. Multiple Sclerosis. Lancet (2018) 391(10130):1622–36. 10.1016/S0140-6736(18)30481-1 - DOI - PubMed
    1. Lublin FD, Reingold SC. Defining the Clinical Course of Multiple Sclerosis: Results of an International Survey. National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. Neurology (1996) 46(4):907–11. 10.1212/wnl.46.4.907 - DOI - PubMed
    1. Koch M, Kingwell E, Rieckmann P, Tremlett H, MS Clinic Neurologists UBC. The Natural History of Secondary Progressive Multiple Sclerosis. J Neurol Neurosurg Psychiatry (2010) 81(9):1039–43. 10.1136/jnnp.2010.208173 - DOI - PubMed

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