000126340 001__ 126340
000126340 005__ 20241125101137.0
000126340 0247_ $$2doi$$a10.1016/j.msard.2023.104725
000126340 0248_ $$2sideral$$a133805
000126340 037__ $$aART-2023-133805
000126340 041__ $$aeng
000126340 100__ $$aOrtiz, M.
000126340 245__ $$aDiagnosis of multiple sclerosis using optical coherence tomography supported by artificial intelligence
000126340 260__ $$c2023
000126340 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126340 5203_ $$aBackground: 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.
000126340 536__ $$9info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726$$9info:eu-repo/grantAgreement/ES/ISCIII/PI20-00437$$9info:eu-repo/grantAgreement/ES/ISCIII-RICORDS/RD21-0002-0050
000126340 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000126340 590__ $$a2.9$$b2023
000126340 592__ $$a0.99$$b2023
000126340 591__ $$aCLINICAL NEUROLOGY$$b102 / 280 = 0.364$$c2023$$dQ2$$eT2
000126340 593__ $$aMedicine (miscellaneous)$$c2023$$dQ1
000126340 593__ $$aNeurology (clinical)$$c2023$$dQ2
000126340 593__ $$aNeurology$$c2023$$dQ2
000126340 594__ $$a5.8$$b2023
000126340 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126340 700__ $$aMallen, V.
000126340 700__ $$aBoquete, L.
000126340 700__ $$aSánchez-Morla, E. M.
000126340 700__ $$aCordón, Beatriz$$uUniversidad de Zaragoza
000126340 700__ $$0(orcid)0000-0001-9411-5834$$aVilades, E.$$uUniversidad de Zaragoza
000126340 700__ $$aDongil-Moreno, F. J.
000126340 700__ $$aMiguel-Jiménez, J. M.
000126340 700__ $$0(orcid)0000-0001-6258-2489$$aGarcía-Martín, E.$$uUniversidad de Zaragoza
000126340 7102_ $$11013$$2646$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Oftalmología
000126340 773__ $$g74 (2023), 104725 [8 pp.]$$pMult. scler. relat. disord.$$tMultiple Sclerosis and Related Disorders$$x2211-0348
000126340 8564_ $$s2338740$$uhttps://zaguan.unizar.es/record/126340/files/texto_completo.pdf$$yVersión publicada
000126340 8564_ $$s2416688$$uhttps://zaguan.unizar.es/record/126340/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126340 909CO $$ooai:zaguan.unizar.es:126340$$particulos$$pdriver
000126340 951__ $$a2024-11-22-12:01:08
000126340 980__ $$aARTICLE