000112767 001__ 112767
000112767 005__ 20240319080953.0
000112767 0247_ $$2doi$$a10.1007/s10439-022-02930-3
000112767 0248_ $$2sideral$$a128187
000112767 037__ $$aART-2022-128187
000112767 041__ $$aeng
000112767 100__ $$0(orcid)0000-0001-7248-4399$$aMontolío, A.$$uUniversidad de Zaragoza
000112767 245__ $$aComparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis
000112767 260__ $$c2022
000112767 5060_ $$aAccess copy available to the general public$$fUnrestricted
000112767 5203_ $$aMachine learning approaches in diagnosis and prognosis of multiple sclerosis (MS) were analysed using retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). A cross-sectional study (72 MS patients and 30 healthy controls) was used for diagnosis. These 72 MS patients were involved in a 10-year longitudinal follow-up study for prognostic purposes. Structural measurements of RNFL thickness were performed using different Spectralis OCT protocols: fast macular thickness protocol to measure macular RNFL, and fast RNFL thickness protocol and fast RNFL-N thickness protocol to measure peripapillary RNFL. Binary classifiers such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested. For MS diagnosis, the best acquisition protocol was fast macular thickness protocol using k-NN (accuracy: 95.8%; sensitivity: 94.4%; specificity: 97.2%; precision: 97.1%; AUC: 0.958). For MS prognosis, our model with a 3-year follow up to predict disability progression 8 years later was the best predictive model. DT performed best for fast macular thickness protocol (accuracy: 91.3%; sensitivity: 90.0%; specificity: 92.5%; precision: 92.3%; AUC: 0.913) and SVM for fast RNFL-N thickness protocol (accuracy: 91.3%; sensitivity: 87.5%; specificity: 95.0%; precision: 94.6%; AUC: 0.913). This work concludes that measurements of RNFL thickness obtained with Spectralis OCT have a good ability to diagnose MS and to predict disability progression in MS patients. This machine learning approach would help clinicians to have valuable information. © 2022, The Author(s).
000112767 536__ $$9info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726$$9info:eu-repo/grantAgreement/ES/MEC/BES-2017-080384$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2016-79302-R
000112767 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000112767 590__ $$a3.8$$b2022
000112767 592__ $$a0.848$$b2022
000112767 591__ $$aENGINEERING, BIOMEDICAL$$b47 / 96 = 0.49$$c2022$$dQ2$$eT2
000112767 593__ $$aBiomedical Engineering$$c2022$$dQ2
000112767 594__ $$a7.7$$b2022
000112767 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000112767 700__ $$0(orcid)0000-0002-2967-6747$$aCegoñino, J.$$uUniversidad de Zaragoza
000112767 700__ $$aGarcia-Martin, E.
000112767 700__ $$0(orcid)0000-0003-0669-777X$$aPérez del Palomar, A.$$uUniversidad de Zaragoza
000112767 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000112767 773__ $$g50, 5 (2022), 507-528$$pAnn. biomed. eng.$$tAnnals of Biomedical Engineering$$x0090-6964
000112767 8564_ $$s2879682$$uhttps://zaguan.unizar.es/record/112767/files/texto_completo.pdf$$yVersión publicada
000112767 8564_ $$s2589507$$uhttps://zaguan.unizar.es/record/112767/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000112767 909CO $$ooai:zaguan.unizar.es:112767$$particulos$$pdriver
000112767 951__ $$a2024-03-18-13:16:03
000112767 980__ $$aARTICLE