000147631 001__ 147631
000147631 005__ 20241220131256.0
000147631 0247_ $$2doi$$a10.1016/j.compbiomed.2021.104416
000147631 0248_ $$2sideral$$a126259
000147631 037__ $$aART-2021-126259
000147631 041__ $$aeng
000147631 100__ $$0(orcid)0000-0001-7248-4399$$aMontolío A.$$uUniversidad de Zaragoza
000147631 245__ $$aMachine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography
000147631 260__ $$c2021
000147631 5060_ $$aAccess copy available to the general public$$fUnrestricted
000147631 5203_ $$aBackground: Multiple sclerosis (MS) is a neurodegenerative disease that affects the central nervous system, especially the brain, spinal cord, and optic nerve. Diagnosis of this disease is a very complex process and generally requires a lot of time. In addition, treatments are applied without any information on the disability course in each MS patient. For these two reasons, the objective of this study was to improve the MS diagnosis and predict the long-term course of disability in MS patients based on clinical data and retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). Material and methods: A total of 104 healthy controls and 108 MS patients, 82 of whom had a 10-year follow-up, were enrolled. Classification algorithms 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 to develop two predictive models: MS diagnosis model and MS disability course prediction model. Results: For MS diagnosis, the best result was obtained using EC (accuracy: 87.7%; sensitivity: 87.0%; specificity: 88.5%; precision: 88.7%; AUC: 0.8775). In line with this good performance, the accuracy was 85.4% using k-NN and 84.4% using SVM. And, for long-term prediction of MS disability course, LSTM recurrent neural network was the most appropriate classifier (accuracy: 81.7%; sensitivity: 81.1%; specificity: 82.2%; precision: 78.9%; AUC: 0.8165). The use of MLR, SVM and k-NN also showed a good performance (AUC = 0.8). Conclusions: This study demonstrated that machine learning techniques, using clinical and OCT data, can help establish an early diagnosis and predict the course of MS. This advance could help clinicians select more specific treatments for each MS patient. Therefore, our findings underscore the potential of RNFL thickness as a reliable MS biomarker. © 2021 The Author(s)
000147631 536__ $$9info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726$$9info:eu-repo/grantAgreement/ES/MICIU/BES-2017-080384$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2016-79302-R
000147631 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000147631 590__ $$a6.698$$b2021
000147631 591__ $$aBIOLOGY$$b13 / 94 = 0.138$$c2021$$dQ1$$eT1
000147631 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b6 / 57 = 0.105$$c2021$$dQ1$$eT1
000147631 591__ $$aENGINEERING, BIOMEDICAL$$b22 / 98 = 0.224$$c2021$$dQ1$$eT1
000147631 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b24 / 112 = 0.214$$c2021$$dQ1$$eT1
000147631 592__ $$a1.309$$b2021
000147631 593__ $$aHealth Informatics$$c2021$$dQ1
000147631 593__ $$aComputer Science Applications$$c2021$$dQ1
000147631 594__ $$a8.2$$b2021
000147631 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000147631 700__ $$aMartín-Gallego A.
000147631 700__ $$0(orcid)0000-0002-2967-6747$$aCegoñino J.$$uUniversidad de Zaragoza
000147631 700__ $$0(orcid)0000-0003-2710-1875$$aOrduna E.$$uUniversidad de Zaragoza
000147631 700__ $$0(orcid)0000-0001-9411-5834$$aVilades E.$$uUniversidad de Zaragoza
000147631 700__ $$0(orcid)0000-0001-6258-2489$$aGarcia-Martin E.$$uUniversidad de Zaragoza
000147631 700__ $$0(orcid)0000-0003-0669-777X$$aPérez del Palomar A.$$uUniversidad de Zaragoza
000147631 7102_ $$11013$$2646$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Oftalmología
000147631 7102_ $$12002$$2647$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Óptica
000147631 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000147631 773__ $$g133 (2021), 104416 [13 pp]$$pComput. biol. med.$$tComputers in biology and medicine$$x0010-4825
000147631 8564_ $$s6712339$$uhttps://zaguan.unizar.es/record/147631/files/texto_completo.pdf$$yVersión publicada
000147631 8564_ $$s2422273$$uhttps://zaguan.unizar.es/record/147631/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000147631 909CO $$ooai:zaguan.unizar.es:147631$$particulos$$pdriver
000147631 951__ $$a2024-12-20-12:00:51
000147631 980__ $$aARTICLE