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For this reason, this study aims to develop a computer\u2010aided method to facilitate diagnosis and prognosis in MS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This paper combines a cross\u2010sectional study of 72 MS patients and 30 healthy control subjects for diagnosis and a 10\u2010year longitudinal study of the same MS patients for the prediction of disability progression, during which the mGCL was measured using optical coherence tomography (OCT). Deep neural networks were used as an automatic classifier.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For MS diagnosis, greatest accuracy (90.3%) was achieved using 17 features as inputs. The neural network architecture comprised the input layer, two hidden layers and the output layer with softmax activation. For the prediction of disability progression 8\u2009years later, accuracy of 81.9% was achieved with a neural network comprising two hidden layers and 400 epochs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We present evidence that by applying deep learning techniques to clinical and mGCL thickness data it is possible to identify MS and predict the course of the disease. This approach potentially constitutes a non\u2010invasive, low\u2010cost, easy\u2010to\u2010implement and effective method.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/aos.15722","type":"journal-article","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T03:20:31Z","timestamp":1686367231000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: A deep learning approach"],"prefix":"10.1111","volume":"102","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7248-4399","authenticated-orcid":false,"given":"Alberto","family":"Montol\u00edo","sequence":"first","affiliation":[{"name":"Biomaterials Group, Aragon Institute of Engineering Research (I3A) University of Zaragoza Zaragoza Spain"},{"name":"Mechanical Engineering Department University of Zaragoza Zaragoza 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