The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

Abstract

Background: Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging.

Methods: Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms.

Results: Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.

Conclusions: ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.

Keywords: Deep learning; Disease progression; Medical informatics; Multiple sclerosis; Prognosis; Supervised machine learning; Systematic review.

Fig. 1

Flow chart of the systematic review process

Fig. 2

Distribution of manuscripts with publication years. The total number of publications adds up to 68 because out of the 66 included publications, one discussed both diagnosis and MS sub-types and another discussed both diagnosis and prognosis

Fig. 3

Sunburst chart of machine learning algorithms applicable to multiple sclerosis studies

Fig. 4

Histogram of machine learning algorithms in multiple sclerosis studies. The y-axis refers to the number of studies

Fig. 5

Sunburst chart of machine learning applications and data in multiple sclerosis studies

Fig. 6

Histogram of data for ML applications. The y-axis refers to the number of studies