{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T17:19:10Z","timestamp":1778001550052,"version":"3.51.4"},"reference-count":83,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T00:00:00Z","timestamp":1640563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Secretariat of State for Research, Development and Innovation; Carlos III Health Institute","award":["DPI2017-88438-R (AEI\/FEDER, EU);PI17\/01726 and PI20\/00437; RD16\/0008\/020, RD16\/0008\/029"],"award-info":[{"award-number":["DPI2017-88438-R (AEI\/FEDER, EU);PI17\/01726 and PI20\/00437; RD16\/0008\/020, RD16\/0008\/029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT). Methods: SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 \u00d7 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN\u2019s training set. Results: The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0. Conclusions: Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.<\/jats:p>","DOI":"10.3390\/s22010167","type":"journal-article","created":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T01:20:43Z","timestamp":1640654443000},"page":"167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation"],"prefix":"10.3390","volume":"22","author":[{"given":"Almudena","family":"L\u00f3pez-Dorado","sequence":"first","affiliation":[{"name":"Biomedical Engineering Group, Department of Electronics, University of Alcal\u00e1, 28801 Alcal\u00e1 de Henares, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miguel","family":"Ortiz","sequence":"additional","affiliation":[{"name":"Computer Vision, Imaging and Machine Intelligence Research Group, Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, 4365 Luxembourg, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda","family":"Satue","sequence":"additional","affiliation":[{"name":"Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Department of Ophthalmology, Aragon Institute for Health Research (IIS Aragon), Miguel Servet University Hospital, University of Zaragoza, 50018 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4009-3075","authenticated-orcid":false,"given":"Mar\u00eda J.","family":"Rodrigo","sequence":"additional","affiliation":[{"name":"Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Department of Ophthalmology, Aragon Institute for Health Research (IIS Aragon), Miguel Servet University Hospital, University of Zaragoza, 50018 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4179-6100","authenticated-orcid":false,"given":"Rafael","family":"Barea","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Group, Department of Electronics, University of Alcal\u00e1, 28801 Alcal\u00e1 de Henares, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eva M.","family":"S\u00e1nchez-Morla","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Hospital 12 de Octubre Research Institute (i+12), 28041 Madrid, Spain"},{"name":"Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain"},{"name":"Biomedical Research Networking Centre in Mental Health (CIBERSAM), 28029 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlo","family":"Cavaliere","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Group, Department of Electronics, University of Alcal\u00e1, 28801 Alcal\u00e1 de Henares, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 M.","family":"Rodr\u00edguez-Ascariz","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Group, Department of Electronics, University of Alcal\u00e1, 28801 Alcal\u00e1 de Henares, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2710-1875","authenticated-orcid":false,"given":"Elvira","family":"Orduna-Hospital","sequence":"additional","affiliation":[{"name":"Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Department of Ophthalmology, Aragon Institute for Health Research (IIS Aragon), Miguel Servet University Hospital, University of Zaragoza, 50018 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luciano","family":"Boquete","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Group, Department of Electronics, University of Alcal\u00e1, 28801 Alcal\u00e1 de Henares, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6258-2489","authenticated-orcid":false,"given":"Elena","family":"Garcia-Martin","sequence":"additional","affiliation":[{"name":"Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Department of Ophthalmology, Aragon Institute for Health Research (IIS Aragon), Miguel Servet University Hospital, University of Zaragoza, 50018 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep Learning Applications in 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