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

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Journal:
International journal of clinical and health psychology

ISSN: 1697-2600

Year of publication: 2022

Volume: 22

Issue: 2

Pages: 31-40

Type: Article

DOI: 10.1016/J.IJCHP.2022.100294 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: International journal of clinical and health psychology

Abstract

Background/Objective 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). Method 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. Results 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. Conclusions This study identifies a potential novel objective and non-invasive biomarker of FM based on retina analysis using SS-OCT.

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