# Machine learning for the diagnosis of fibromyalgia based on magnetic resonance imaging

## TL;DR

This study uses brain imaging and machine learning to improve the diagnosis of fibromyalgia, a condition marked by widespread pain.

## Contribution

The paper introduces a novel diagnostic method combining rs-fMRI, DTI, and machine learning for fibromyalgia.

## Key findings

- Differences in brain image indices were found in the temporal and frontal lobes between FM patients and healthy controls.
- A classification model using DTI features and a support vector machine achieved superior diagnostic performance.
- Combining DTI with machine learning improves the accuracy of FM diagnosis.

## Abstract

The clinical diagnosis of fibromyalgia (FM), a syndrome characterized by generalized pain, is challenging due to its unknown etiology and frequent comorbidity with other diseases. As a noninvasive modality, functional magnetic resonance imaging has been extensively employed in investigating the pathogenesis of FM. This study proposes a novel diagnostic approach utilizing resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) combined with a machine learning algorithm with the objective of enhancing the clinical diagnostic efficiency of FM. Two-sample t tests revealed differences between FM patients and healthy controls in rs-fMRI and DTI corresponding to brain image indices, mainly in the temporal lobe and frontal lobe. In addition, an effective diagnostic classification model was developed based on the single variable feature selection method by applying a support vector and random forest classifier combined with different brain image indicators. Our study demonstrated that the integration of DTI features with a support vector machine model yields superior diagnostic outcomes.