Objective diagnosis of fibromyalgia using neuroretinal evaluation and artificial intelligence

  1. Luciano Boquete 1
  2. Maria-José Vicente 2
  3. Juan-Manuel Miguel-Jiménez 1
  4. Eva-María Sánchez-Morla 3
  5. Miguel Ortiz 4
  6. Maria Satue 2
  7. Elena Garcia-Martin 2
  1. 1 Universidad de Alcalá
    info

    Universidad de Alcalá

    Alcalá de Henares, España

    ROR https://ror.org/04pmn0e78

  2. 2 Universidad de Zaragoza
    info

    Universidad de Zaragoza

    Zaragoza, España

    ROR https://ror.org/012a91z28

  3. 3 Hospital Universitario 12 de Octubre
    info

    Hospital Universitario 12 de Octubre

    Madrid, España

    ROR https://ror.org/00qyh5r35

  4. 4 University of Luxembourg
    info

    University of Luxembourg

    Ciudad de Luxemburgo, Luxemburgo

    ROR https://ror.org/036x5ad56

Mostrar afiliaciones +
Revista:
International journal of clinical and health psychology

ISSN: 1697-2600

Año de publicación: 2022

Volumen: 22

Número: 2

Páginas: 31-40

Tipo: Artículo

DOI: 10.1016/J.IJCHP.2022.100294 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: International journal of clinical and health psychology

Resumen

Antecedentes/Objetivo Identificar biomarcadores objetivos de fibromialgia (FM) aplicando inteligencia artificial a datos estructurales de retina obtenidos mediante tomografía de coherencia óptica Swept Source (TCO-SS). Método 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. Resultados 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). Conclusiones Se identifica un potencial biomarcador objetivo y no invasivo para el diagnóstico de FM basado en el análisis de la neurorretina mediante TCO-SS.

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