Diagnosis and Analysis of Multiple Sclerosis Disease Using Artificial Intelligence

Multiple sclerosis (MS) is a chronic acquired idiopathic, autoimmune inflammatory demyelinating disorder of the central nervous system (CNS) that results in disruption of myelin sheath due to environmental and genetic factors. Demyelination and inflammation occur at the early stages of plaque formation which progresses to axonal damage and neuronal loss in the later stages of the disease. Despite having a typical progression there are no specific markers that can help in its easy and early diagnosis. Consequently, MS diagnosis primarily depends on the medical history of the patient, neurological examination including MRI scan, evoked potential test, Lumbar puncture, and some blood. However, it still takes some years to make a correct diagnosis since the condition worsens gradually. Moreover, the last decade has seen a significant rise in MS. However, advances in the applications of information technology and their intertwining with the medical domain have accounted for a considerable amount of data from normal and diseased individuals alike. More recently, artificial intelligence (AI) techniques in MS research have increased interest and provide means to facilitate its early diagnosis and better prognosis. The traditional methods, on the contrary, have relatively lower values of performance measures that reduce the effectiveness of the method. The AI approaches can make efficient utilization of observational data for early identification and prediction of the disease progression. In this paper, we have discussed the different ways through which AI techniques can help in the prognosis and early diagnosis of MS and the associated challenges of AI methods in MS research.