Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques.

Journal: PloS one
Published Date: Jan 1, 2019

Abstract

OBJECTIVE: 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.

Authors

  • Amaya Pérez Del Palomar
    Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
  • José Cegoñino
    Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
  • Alberto Montolío
    Group of Biomaterials, Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
  • Elvira Orduna
    Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain.
  • Elisa Vilades
    Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain.
  • Berta Sebastián
    Department of Neurology, Miguel Servet University Hospital, Zaragoza, Spain.
  • Luis E Pablo
  • Elena García-Martín

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