000087604 001__ 87604
000087604 005__ 20200716101544.0
000087604 0247_ $$2doi$$a10.3390/s19235323
000087604 0248_ $$2sideral$$a115818
000087604 037__ $$aART-2019-115818
000087604 041__ $$aeng
000087604 100__ $$aCavaliere, C.
000087604 245__ $$aComputer-aided diagnosis of multiple sclerosis using a support vector machine and optical coherence tomography features
000087604 260__ $$c2019
000087604 5060_ $$aAccess copy available to the general public$$fUnrestricted
000087604 5203_ $$aThe purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of optic neuritis and forty-eight healthy control subjects were selected. Swept-source optical coherence tomography (SS-OCT) was performed using a DRI (deep-range imaging) Triton OCT device (Topcon Corp., Tokyo, Japan). Mean values (right and left eye) for macular thickness (retinal and choroidal layers) and peripapillary area (retinal nerve fibre layer, retinal, ganglion cell layer—GCL, and choroidal layers) were compared between both groups. Based on the analysis of the area under the receiver operator characteristic curve (AUC), the 3 variables with the greatest discriminant capacity were selected to form the feature vector. A SVM was used as an automatic classifier, obtaining the confusion matrix using leave-one-out cross-validation. Classification performance was assessed with Matthew’s correlation coefficient (MCC) and the AUCCLASSIFIER. The most discriminant variables were found to be the total GCL++ thickness (between inner limiting membrane to inner nuclear layer boundaries), evaluated in the peripapillary area and macular retina thickness in the nasal quadrant of the outer and inner rings. Using the SVM classifier, we obtained the following values: MCC = 0.81, sensitivity = 0.89, specificity = 0.92, accuracy = 0.91, and AUCCLASSIFIER = 0.97. Our findings suggest that it is possible to classify control subjects and MS patients without previous optic neuritis by applying machine-learning techniques to study the structural neurodegeneration in the retina.
000087604 536__ $$9info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726$$9info:eu-repo/grantAgreement/ES/ISCIII/RETICS-RD16-0008-020$$9info:eu-repo/grantAgreement/ES/ISCIII/RETICS-RD16-0008-029$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2017-88438-R
000087604 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000087604 590__ $$a3.275$$b2019
000087604 591__ $$aCHEMISTRY, ANALYTICAL$$b22 / 86 = 0.256$$c2019$$dQ2$$eT1
000087604 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b15 / 64 = 0.234$$c2019$$dQ1$$eT1
000087604 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b77 / 266 = 0.289$$c2019$$dQ2$$eT1
000087604 592__ $$a0.653$$b2019
000087604 593__ $$aInstrumentation$$c2019$$dQ1
000087604 593__ $$aAtomic and Molecular Physics, and Optics$$c2019$$dQ2
000087604 593__ $$aMedicine (miscellaneous)$$c2019$$dQ2
000087604 593__ $$aInformation Systems$$c2019$$dQ2
000087604 593__ $$aAnalytical Chemistry$$c2019$$dQ2
000087604 593__ $$aElectrical and Electronic Engineering$$c2019$$dQ2
000087604 593__ $$aBiochemistry$$c2019$$dQ3
000087604 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000087604 700__ $$aVilades, E.
000087604 700__ $$aAlonso-Rodríguez, M.A.C.
000087604 700__ $$aRodrigo, M.J.
000087604 700__ $$0(orcid)0000-0003-2389-8282$$aPablo, L.E.$$uUniversidad de Zaragoza
000087604 700__ $$aMiguel, J.M.
000087604 700__ $$aLópez-Guillén, E.
000087604 700__ $$aSánchez Morla, E.M.A.
000087604 700__ $$aBoquete, L.
000087604 700__ $$0(orcid)0000-0001-6258-2489$$aGarcia-Martin, E.$$uUniversidad de Zaragoza
000087604 7102_ $$11004$$2646$$aUniversidad de Zaragoza$$bDpto. Cirugía,Ginecol.Obstetr.$$cÁrea Oftalmología
000087604 773__ $$g19, 23 (2019), 5323 [17 pp.]$$pSensors$$tSensors (Switzerland)$$x1424-8220
000087604 8564_ $$s1822273$$uhttps://zaguan.unizar.es/record/87604/files/texto_completo.pdf$$yVersión publicada
000087604 8564_ $$s107671$$uhttps://zaguan.unizar.es/record/87604/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000087604 909CO $$ooai:zaguan.unizar.es:87604$$particulos$$pdriver
000087604 951__ $$a2020-07-16-09:42:02
000087604 980__ $$aARTICLE