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

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.