000096025 001__ 96025
000096025 005__ 20201029165924.0
000096025 0247_ $$2doi$$a10.1371/journal.pone.0216410
000096025 0248_ $$2sideral$$a112491
000096025 037__ $$aART-2019-112491
000096025 041__ $$aeng
000096025 100__ $$0(orcid)0000-0003-0669-777X$$aPérez del Palomar, Amaya$$uUniversidad de Zaragoza
000096025 245__ $$aSwept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques
000096025 260__ $$c2019
000096025 5060_ $$aAccess copy available to the general public$$fUnrestricted
000096025 5203_ $$aObjective
To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the capability of machine learning techniques to improve the detection of retinal nerve fiber layer (RNFL) and the complex Ganglion Cell Layer–Inner plexiform layer (GCL+) damage in patients with multiple sclerosis and to use the SS-OCT as a biomarker to early predict this disease.
Methods
Patients with relapsing-remitting MS (n = 80) and age-matched healthy controls (n = 180) were enrolled. Different protocols from the DRI SS-OCT Triton system were used to obtain the RNFL and GCL+ thicknesses in both eyes. Macular and peripapilar areas were analyzed to detect the zones with higher thickness decrease. The performance of different machine learning techniques (decision trees, multilayer perceptron and support vector machine) for identifying RNFL and GCL+ thickness loss in patients with MS were evaluated. Receiver-operating characteristic (ROC) curves were used to display the ability of the different tests to discriminate between MS and healthy eyes in our population.
Results
Machine learning techniques provided an excellent tool to predict MS disease using SS-OCT data. In particular, the decision trees obtained the best prediction (97.24%) using RNFL data in macular area and the area under the ROC curve was 0.995, while the wide protocol which covers an extended area between macula and papilla gave an accuracy of 95.3% with a ROC of 0.998. Moreover, it was obtained that the most significant area of the RNFL to predict MS is the macula just surrounding the fovea. On the other hand, in our study, GCL+ did not contribute to predict MS and the different machine learning techniques performed worse in this layer than in RNFL.
Conclusions
Measurements of RNFL thickness obtained with SS-OCT have an excellent ability to differentiate between healthy controls and patients with MS. Thus, the use of machine learning techniques based on these measures can be a reliable tool to help in MS diagnosis.
000096025 536__ $$9info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726$$9info:eu-repo/grantAgreement/ES/MCIU/MAT2017-83858-C2-2$$9info:eu-repo/grantAgreement/ES/MEC/BES-2017-080384$$9info:eu-repo/grantAgreement/ES/MICINN/DPI2016-79302-R
000096025 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000096025 590__ $$a2.74$$b2019
000096025 591__ $$aMULTIDISCIPLINARY SCIENCES$$b27 / 71 = 0.38$$c2019$$dQ2$$eT2
000096025 592__ $$a1.023$$b2019
000096025 593__ $$aMultidisciplinary$$c2019$$dQ1
000096025 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000096025 700__ $$0(orcid)0000-0002-2967-6747$$aCegoñino, José$$uUniversidad de Zaragoza
000096025 700__ $$aMontolío, Alberto
000096025 700__ $$0(orcid)0000-0003-2710-1875$$aOrduna, Elvira$$uUniversidad de Zaragoza
000096025 700__ $$aVilades, Elisa
000096025 700__ $$aSebastián, Berta
000096025 700__ $$0(orcid)0000-0003-2389-8282$$aPablo, Luis E.$$uUniversidad de Zaragoza
000096025 700__ $$0(orcid)0000-0001-6258-2489$$aGarcia-Martin, Elena$$uUniversidad de Zaragoza
000096025 7102_ $$11004$$2646$$aUniversidad de Zaragoza$$bDpto. Cirugía,Ginecol.Obstetr.$$cÁrea Oftalmología
000096025 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000096025 773__ $$g14, 5 (2019), [18 pp]$$pPLoS One$$tPloS one$$x1932-6203
000096025 8564_ $$s676807$$uhttps://zaguan.unizar.es/record/96025/files/texto_completo.pdf$$yVersión publicada
000096025 8564_ $$s472501$$uhttps://zaguan.unizar.es/record/96025/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000096025 909CO $$ooai:zaguan.unizar.es:96025$$particulos$$pdriver
000096025 951__ $$a2020-10-29-14:06:35
000096025 980__ $$aARTICLE