Machine learning classification of multiple sclerosis in children using optical coherence tomography.

ABSTRACT:

Background

In children, multiple sclerosis (MS) is the ultimate diagnosis in only 1/5 to 1/3 of cases after a first episode of central nervous system (CNS) demyelination. As the visual pathway is frequently affected in MS and other CNS demyelinating disorders (DDs), structural retinal imaging such as optical coherence tomography (OCT) can be used to differentiate MS.

Objective

This study aimed to investigate the utility of machine learning (ML) based on OCT features to identify distinct structural retinal features in children with DDs.

Methods

This study included 512 eyes from 187 (n_eyes = 374) children with demyelinating diseases and 69 (n_eyes = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT featur

SUBMITTER: Ciftci Kavaklioglu B 

PROVIDER: S-EPMC9679797 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature