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

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.

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 are

SUBMITTER: Perez Del Palomar A 

PROVIDER: S-EPMC6502323 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature