Objective Diagnosis of Fibromyalgia Using Neuroretinal Evaluation and Artificial Intelligence.
| Autor: | Boquete L; Biomedical Engineering Group, Department of Electronics, University of Alcalá, Spain., Vicente MJ; Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, and Miguel Servet Ophthalmology Research Group (GIMSO), Aragon Health Research Institute (IIS Aragon), University of Zaragoza, Spain., Miguel-Jiménez JM; Biomedical Engineering Group, Department of Electronics, University of Alcalá, Spain., Sánchez-Morla EM; Department of Psychiatry, Hospital 12 de Octubre Research Institute (i+12), Spain.; Faculty of Medicine, Complutense University of Madrid, Spain.; CIBERSAM: Biomedical Research Networking Centre in Mental Health, Spain., Ortiz M; Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg., Satue M; Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, and Miguel Servet Ophthalmology Research Group (GIMSO), Aragon Health Research Institute (IIS Aragon), University of Zaragoza, Spain., Garcia-Martin E; Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, and Miguel Servet Ophthalmology Research Group (GIMSO), Aragon Health Research Institute (IIS Aragon), University of Zaragoza, Spain. |
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| Jazyk: | angličtina |
| Zdroj: | International journal of clinical and health psychology : IJCHP [Int J Clin Health Psychol] 2022 May-Aug; Vol. 22 (2), pp. 100294. Date of Electronic Publication: 2022 Feb 23. |
| DOI: | 10.1016/j.ijchp.2022.100294 |
| Abstrakt: | 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. (© 2022 Published by Elsevier España, S.L.U.) |
| Databáze: | MEDLINE |
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