Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)

Abstract

Computer-aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence-based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.

Keywords: clinical isolated syndrome; deep learning; magnetic resonance imaging/diagnosis; multiple sclerosis.

Figure 1

A schematic generalized workflow of the DL techniques presented in the manuscript. Initially the magnetic resonance acquisition is performed, producing a number of different anatomical and functional map representations. These provide additional information regarding the underlying pathophysiology (for example, quantitative), which can be used as input in the DL architecture for training the models and, in turn, address un-met clinical needs. DL, deep learning.

Figure 2

Pie chart distribution, grouping reviewed studies according to different end-points. The vast majority of published articles are serving segmentation and classification techniques.

Figure 3

Number of publications, in each application category, per year. There is a trend indicating that the number of publications in the segmentation and classification categories are increasing in recent years.

Figure 4

Study limitations of the relevant publications presented in a pie chart. Most common limitations in the reviewed research article were the small training cohort and the lack of ground truth.