Objective Diagnosis of Fibromyalgia Using Neuroretinal Evaluation and Artificial Intelligence

Abstract

This study aims to identify objective biomarkers of fibromyalgia (FM) by applying artificial intelligence algorithms to structural data on the neuroretina obtained using swept-source optical coherence tomography (SS-OCT). The study cohort comprised 29 FM patients and 32 control subjects. The thicknesses of complete retina, 3 retinal layers [ganglion cell layer (GCL+), GCL++ (between the inner limiting membrane and the inner nuclear layer boundaries) and retinal nerve fiber layer (RNFL)] and choroid in 9 areas around the macula were obtained using SS-OCT. Discriminant capacity was evaluated using the area under the curve (AUC) and the Relief algorithm. A diagnostic aid system with an automatic classifier was implemented. No significant difference (p ≥ .660) was found anywhere in the choroid. In the RNFL, a significant difference was found in the inner inferior region (p = .010). In the GCL+, GCL++ layers and complete retina, a significant difference was found in the 4 regions defining the inner ring: temporal, superior, nasal and inferior. Applying an ensemble RUSBoosted tree classifier to the features with greatest discriminant capacity achieved accuracy = .82 and AUC = .82. This study identifies a potential novel objective and non-invasive biomarker of FM based on retina analysis using SS-OCT. Identificar biomarcadores objetivos de fibromialgia (FM) aplicando inteligencia artificial a datos estructurales de retina obtenidos mediante tomografía de coherencia óptica Swept Source (TCO-SS). Se evaluó una cohorte de 29 pacientes con FM y otra de 32 sujetos control, registrando los espesores de la retina completa, de varias capas de la retina [capa de células ganglionares (CCG+), CCG ampliada (CCG++, entre la membrana limitante interna y los límites de la capa nuclear interna) y capa de fibras nerviosas (CFNR)] y de la coroides, mediante TCO-SS. La capacidad discriminante se evaluó mediante el área bajo la curva ROC (AROC) y el algoritmo Relief. Se implementó un sistema de ayuda al diagnóstico con clasificador automático. No se observó diferencia significativa (p ≥ .660) en la coroides, pero sí en el sector inferior del anillo interno de la CFNR (p = .010) y en los cuatro sectores del anillo interno en las capas CCG+, CCG++ y retina completa. Utilizando un árbol de decisión ensemble RUSBoosted como clasificador de las características con mayor capacidad discriminante, se obtuvo una predicción alta (AROC=.820). Se identifica un potencial biomarcador objetivo y no invasivo para el diagnóstico de FM basado en el análisis de la neurorretina mediante TCO-SS.

Topics & Concepts