Evidence map›Paper›PMID 35946086›Full record

ArticleMultiple sclerosis (Houndmills, Basingstoke, England)2022

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

Beyza Ciftci Kavaklioglu, Lauren Erdman, Anna Goldenberg, Can Kavaklioglu, Cara Alexander, Hannah M Oppermann, Amish Patel, Soaad Hossain, Tara Berenbaum, Olivia Yau and 8 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Multiple sclerosis (Houndmills, Basingstoke, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.9field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 23 citations in OpenAlex.

  1. AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  2. Review
  3. Article
  4. Semi-Supervised Learning for Predicting Multiple Sclerosis.Journal of personalized medicine · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors at 5 institutions in 3 countries.

Beyza Ciftci KavakliogluNeuroscience 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.
Lauren ErdmanDepartment of Computer Science, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada.
Anna GoldenbergDepartment 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.
Can KavakliogluDepartment of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON, Canada.
Cara AlexanderDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.
Hannah M OppermannDepartment of Computer Science, University of Toronto, Toronto, ON, Canada/Department of Information and Computing Sciences, Utrecht University, Utrecht, the Netherlands.
Amish PatelDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.
Soaad HossainDepartment 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.
Tara BerenbaumDivision of Neurology, Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada.
Olivia YauDivision of Neurology, Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada.
Carmen YeaDivision of Neurology, Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada.
Mina LyDivision of Neurology, Department of Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada.
Fiona CostelloDepartment of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada/Department of Surgery (Ophthalmology), University of Calgary, Calgary, AB, Canada.
Jean K MahDepartment Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Arun ReginaldDepartment 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.
Brenda BanwellDivision of Neurology, The Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Giulia LongoniSickKids 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.
E Ann YehSickKids 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.ORCID 0000-0002-5393-7417
University of Toronto · CAHospital for Sick Children · CAUniversity of Calgary · CAChildren's Hospital of Philadelphia · USToronto Metropolitan University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn 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.

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

methodsThis study included 512 eyes from 187 (

resultsRandom 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.

conclusionThis study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children.

Indexed as

Multiple SclerosisTomography, Optical CoherenceChildHumansMachine LearningRetinaVisual PathwaysMultiple sclerosisoptical coherence tomographypediatricretinal nerve fiber layer thicknesssupervised learning

Identifiers

PMID35946086
PMCPMC9679797
OpenAlexW4292466559

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.