A useful tool
In the field of MS, most of the studies performed with machine learning have focused on the analysis of radiological images. However, others are now looking at the blood transcriptome as a potential path to better classifying a highly complex disease with substantial heterogeneity in presentation, progression, and outcome.
For example, machine learning of the blood transcriptome has also shown high accuracy in the diagnosis and classification of MS in patients with clinically isolated syndrome (CIS). One study, published in Cell Reports Medicine, was led by Cinthia Farina, PhD, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan.
Although she did not hear the presentation by Dr. Gurevich, Dr. Farina is enthusiastic about the potential for machine learning to help manage MS through the analysis of the blood transcriptome. “I do believe that transcriptomics in peripheral immune cells may become a useful tool for MS diagnosis and prognosis,” she said.
In her own study, in which machine learning algorithms were developed and trained on the basis of peripheral blood from patients with CIS, the tool proved accurate with a strong potential for being incorporated into routine clinical management.
“Machine learning applied to the blood transcriptomes was extremely efficient with a 95.6% accuracy in discriminating PPMS from RRMS [relapsing-remitting] MS,” she reported.
Dr. Gurevich has no potential financial conflicts of interest to report. He reported funding for the study was provided by Roche. Dr. Farina reports financial relationships with Merck-Serono, Novartis, and Teva.