Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images
Faezeh Moazami, Alain Lefèvre‐Utile, Costas Papaloukas, Vassili Soumelis
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.
Topics & Concepts
Multiple sclerosisMagnetic resonance imagingMedicineDiseaseRadiologyComputer sciencePathologyImmunologyMultiple Sclerosis Research StudiesDigital Imaging for Blood DiseasesSpectroscopy Techniques in Biomedical and Chemical Research