AI Case Study

Researchers aim to identify fibromyalgia patients from fMRI images using machine learning

Researchers from the University of Colorado Boulder, Hospital del Mar in Spain, Autonomous University of Barcelona and The University of Melbourne have developed a system aimed at identifying a brain signature that characterises fibromyalgia (FM). Using fMRI machines to capture images of the brain signals, the system uses machine learning to distinguish the brain scans of those with fibromyalgia from those without. When tested with fMRI images of the brain signals of 37 fibromyalgia patients and 35 healthy people used as a control group, the system was able to distinguish between the two with with 93% accuracy.

Industry

Public And Social Sector

Education And Academia

Project Overview

"The researchers who successfully used machine learning to identify fibromyalgia patients started by using fMRI machines to capture images of the brain signals of 37 fibromyalgia patients and 35 healthy people used as a control group. All the participants had pressure applied to their right thumbnail to create “severe but tolerable pain,” explained the researchers in their paper, published in the journal Pain last year. Those with fibromyalgia experienced more pain compared to the healthy controls, according to a neurological signature of physical pain, as well as different activity in the insula area of the brain, related to sensory integration, and the medial prefrontal cortex, which is important for emotional regulation. Collectively, these different neurological responses created a brain signature for fibromyalgia patients. A machine-learning algorithm that was programmed to recognize this neurological signature was able to use it to predict which brain scans were indicative of fibromyalgia and which were not.

As such, neuroimaging combined with artificial intelligence was able to create an objective snapshot of what, to date, has been characterized as a subjective sensation. It made perceptible an experience that was previously unknowable to anyone but the patient.

This study was small, and it will take years, likely at least a decade, before such techniques can be used in a clinical setting. The findings from this small dataset cannot be extrapolated or applied to other patients, and so researchers will need to repeat the process with thousands more."

Reported Results

"Researchers used machine learning to distinguish the brain scans of those with fibromyalgia from those without—with 93% accuracy.

The implications are immense: Decoding the brain signature for fibromyalgia could hold the key to understanding the disease and which treatments work for which patients. But it’s also a definitive, objective sign that fibromyalgia really does exist."

Technology

Function

R And D

Core Research And Development

Background

"There’s no accepted criteria for diagnosing fibromyalgia. There is no known biological malfunction, nor is there any biomarker that can be uncovered in a lab test. Patients experience pain all over their body, fatigue, insomnia, difficulty focusing, depression, and 18 “tender points”—including the back of the neck, elbows, and knees—that are sore to the touch. Antidepressants, painkillers, physical therapy, acupuncture, massage, counseling, and exercise are all used to treat the condition, with varying effects."

Benefits

Data

"fMRI machines to capture images of the brain signals of 37 fibromyalgia patients and 35 healthy people used as a control group."