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Related Concept Videos

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Related Experiment Video

Updated: Sep 2, 2025

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats
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Machine learning classification of multiple sclerosis in children using optical coherence tomography.

Beyza Ciftci Kavaklioglu1, Lauren Erdman2, Anna Goldenberg3

  • 1Neuroscience and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, ON, Canada/Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.

Multiple Sclerosis (Houndmills, Basingstoke, England)
|August 10, 2022
PubMed
Summary

Machine learning using optical coherence tomography (OCT) features can help diagnose multiple sclerosis (MS) in children. This approach aids in differentiating MS from other central nervous system demyelinating disorders (DDs) based on retinal imaging.

Keywords:
Multiple sclerosisoptical coherence tomographypediatricretinal nerve fiber layer thicknesssupervised learning

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Area of Science:

  • Ophthalmology
  • Neurology
  • Artificial Intelligence

Background:

  • Multiple sclerosis (MS) diagnosis in children is challenging after initial central nervous system (CNS) demyelination episodes.
  • Retinal imaging, specifically optical coherence tomography (OCT), offers potential for differentiating MS due to frequent visual pathway involvement.

Purpose of the Study:

  • To evaluate the effectiveness of machine learning (ML) algorithms utilizing OCT features for identifying structural retinal differences in pediatric demyelinating diseases (DDs).

Main Methods:

  • Analysis of 512 eyes from 187 children with DDs and 138 controls.
  • Utilized 24 auto-segmented OCT features as input for ML models.
  • Employed a Random Forest classifier with recursive feature elimination.

Main Results:

  • The ML model achieved 75% accuracy in identifying DDs and 80% accuracy for MS.
  • Distinguishing between MS and monophasic DDs showed 64% accuracy.
  • Identified eight key retinal features crucial for classification.

Conclusions:

  • Machine learning analysis of OCT features shows promise in supporting the diagnosis of MS in pediatric patients.
  • OCT-based ML can aid in differentiating demyelinating disorders in children.