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

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.

Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective

“…( Vrenken et al, 2021 ) To date, most of studies applying AI to MRI data in MS used T2- and T1-weighted imaging, or advanced structural MRI techniques, such as susceptibility-weighted and diffusion-weighted imaging. ( Vrenken et al, 2021 , Moazami et al, 2021 ) Nevertheless, recent investigations showed promising results also when applying AI to fMRI data. For instance, Saccà et al ( Saccà et al, 2019 ) applied five different machine learning techniques to maps of the sensorimotor network (reconstructed by independent component analysis) in 18 people with early MS and 19 HC.…”

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis

“…As one of the most puzzling neurodegenerative disorders, multiple sclerosis (MS) is characterized by a complex biological etiology [1] and a highly heterogeneous disability progression. This gives rise to an important unmet need that has been given considerable attention in MS research in recent decades, which is the prediction of its future course [2][3][4][5]. In light of an ongoing paradigm shift in medicine, moving from a disease-centered to a patient-centered approach [6], the ability to foresee disability build-up in a specific patient would be a true game changer in modern medicine; neurologists could intervene at an early stage, whereas patients and their caregivers could anticipate future challenges in daily life.…”

“…Although the use of machine learning for cognitive prognosis is still in its infancy, this paper aims to offer directions in this field by (1) introducing the concept of machine learning, (2) outlining the pitfalls of machine learning in medical sciences, (3) offering guidance for the design of studies that use ML for cognitive prognosis using lessons learned from ML-powered physical prognosis, (4) summarizing literature on ML-powered cognitive prognostication, and (5) highlighting trends in ML that could boost the field of MS prognosis. Since the main goal of this review is to provide directions for a young field of research rather than to synthesize the scarcely available literature, this review adopts a narrative, non-systematic design.…”

Viral Proteins with PxxP and PY Motifs May Play a Role in Multiple Sclerosis

“…RRMS can then lead to secondary progressive MS (SPMS). This is often accompanied by gradual neurologic deterioration not associated with acute attacks [4]. Finally, some patients experience primary-progressive MS (PPMS), where the disease progresses slowly and steadily, without any remission periods or progressiverelapsing MS (PRMS), which is like PPMS but with periods of relapse in between.…”