Diagnosis of multiple sclerosis using optical coherence tomography supported by artificial intelligence.

Autor: Ortiz M; School of Physics, University of Melbourne, Melbourne, VIC 3010, Australia., Mallen V; Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain; Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain., Boquete L; Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain., Sánchez-Morla EM; Faculty of Medicine, Complutense University of Madrid, Madrid 28040, Spain., Cordón B; Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain; Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain., Vilades E; Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain; Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain., Dongil-Moreno FJ; Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain., Miguel-Jiménez JM; Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain., Garcia-Martin E; Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain; Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain. Electronic address: egmvivax@yahoo.com.
Jazyk: angličtina
Zdroj: Multiple sclerosis and related disorders [Mult Scler Relat Disord] 2023 Jun; Vol. 74, pp. 104725. Date of Electronic Publication: 2023 Apr 17.
DOI: 10.1016/j.msard.2023.104725
Abstrakt: Background: Current procedures for diagnosing multiple sclerosis (MS) present a series of limitations, making it critically important to identify new biomarkers. The aim of the study was to identify new biomarkers for the early diagnosis of MS using spectral-domain optical coherence tomography (OCT) and artificial intelligence.
Methods: Spectral domain OCT was performed on 79 patients with relapsing-remitting multiple sclerosis (RRMS) (disease duration ≤ 2 years, no history of optic neuritis) and on 69 age-matched healthy controls using the posterior pole protocol that incorporates the anatomic Positioning System. Median retinal thickness values in both eyes and inter-eye difference in healthy controls and patients were evaluated by area under the receiver operating characteristic (AUROC) curve analysis in the foveal, parafoveal and perifoveal areas and in the overall area spanned by the three rings. The structures with the greatest discriminant capacity - retinal thickness and inter-eye difference - were used as inputs to a convolutional neural network to assess the diagnostic capability.
Results: Analysis of retinal thickness and inter-eye difference in RRMS patients revealed that greatest alteration occurred in the ganglion cell (GCL), inner plexiform (IPL), and inner retinal (IRL) layers. By using the average thickness of the GCL (AUROC = 0.82) and the inter-eye difference in the IPL (AUROC = 0.71) as inputs to a two-layer convolutional neural network, automatic diagnosis attained accuracy = 0.87, sensitivity = 0.82, and specificity = 0.92.
Conclusion: This study adds weight to the argument that neuroretinal structure analysis could be incorporated into the diagnostic criteria for MS.
Competing Interests: Declaration of Competing Interest The authors report no competing interests.
(Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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