Machine learning classification of multiple sclerosis in children using optical coherence tomography-Reference-Cited by-同舟云学术

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

Published:2022-08-09 Issue:14 Volume:28 Page:2253-2262
ISSN:1352-4585
Container-title:Multiple Sclerosis Journal
language:en
Short-container-title:Mult Scler

Author:

Ciftci Kavaklioglu Beyza¹,Erdman Lauren²,Goldenberg Anna³,Kavaklioglu Can⁴,Alexander Cara⁵,Oppermann Hannah M⁶,Patel Amish⁵,Hossain Soaad⁷,Berenbaum Tara⁸,Yau Olivia⁸,Yea Carmen⁸,Ly Mina⁸,Costello Fiona⁹,Mah Jean K¹⁰,Reginald Arun¹¹,Banwell Brenda¹²,Longoni Giulia¹³,Ann Yeh E¹³¹⁴^ORCID

Affiliation:

1. Neuroscience 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

2. Department of Computer Science, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada

3. Department of Computer Science, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada/Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada

4. Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON, Canada

5. Department of Computer Science, University of Toronto, Toronto, ON, Canada

6. Department of Computer Science, University of Toronto, Toronto, ON, Canada/Department of Information and Computing Sciences, Utrecht University, Utrecht, the Netherlands

7. Department of Computer Science, University of Toronto, Toronto, ON, Canada/Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada/Environics Analytics, Toronto, ON, Canada

8. Division of Neurology, Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada

9. Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada/Department of Surgery (Ophthalmology), University of Calgary, Calgary, AB, Canada

10. Department Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada

11. Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada/Department of Ophthalmology and Vision Sciences, The Hospital for Sick Children, Toronto, ON, Canada

12. Division of Neurology, The Children’s Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

13. SickKids Research Institute, Neuroscience and Mental Health Program, The Hospital for Sick Children, Toronto, ON, Canada/Division of Neurology, The Hospital for Sick Children, Toronto, ON, Canada/Department of Pediatrics, University of Toronto, Toronto, ON, Canada

14. Neuroscience and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada

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 ( neyes = 374) children with demyelinating diseases and 69 ( neyes = 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.

Publisher

SAGE Publications

Subject

Neurology (clinical),Neurology

Link

http://journals.sagepub.com/doi/pdf/10.1177/13524585221112605

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