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Published on: January 10, 2019
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.
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 = 374) children with demyelinating diseases and 69 (n = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT features.
Results:
Random Forest classifier with recursive feature elimination yielded the highest predictive values and identified DDs with 75% and MS with 80% accuracy, while multiclass distinction between MS and monophasic DD was performed with 64% accuracy. A set of eight retinal features were identified as the most important features in this classification.
Conclusion:
This study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children.
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