Evidence map›Paper›PMID 37400574›Full record

ArticleScientific reports2023

Brain imaging signatures of neuropathic facial pain derived by artificial intelligence.

Timur H Latypov, Matthew C So, Peter Shih-Ping Hung, Pascale Tsai, Matthew R Walker, Sarasa Tohyama, Marina Tawfik, Frank Rudzicz, Mojgan Hodaie

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

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.1field-weighted citation impact, top 9% 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

7 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
  2. The Brain-Age Gap in Pediatric Dystonia: Neuroanatomical Deviations Inform Deep Brain Stimulation Outcomes.Movement disorders : official journal of the Movement Disorder Society · 2026
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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

9 authors at 6 institutions in 2 countries.

Timur H LatypovDivision of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Matthew C SoMax Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada.
Peter Shih-Ping HungDivision of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Pascale TsaiMichael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
Matthew R WalkerDivision of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Sarasa TohyamaA.A. Martinos Center for Biomedical Imaging, Harvard Medical School, Charlestown, MA, USA.
Marina TawfikCollaborative Program in Neuroscience, University of Toronto, Toronto, ON, Canada.
Frank RudziczDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.
Mojgan HodaieDivision of Brain, Imaging & Behaviour, Krembil Research Institute, University Health Network, Toronto, ON, Canada. mojgan.hodaie@uhn.ca.ORCID 0000-0001-6278-4929
University Health Network · CAUniversity of Toronto · CADalhousie University · CAHarvard University · USMcMaster University · CAUniversity of Manitoba · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in neuroimaging have permitted the non-invasive examination of the human brain in pain. However, a persisting challenge is in the objective differentiation of neuropathic facial pain subtypes, as diagnosis is based on patients' symptom descriptions. We use artificial intelligence (AI) models with neuroimaging data to distinguish subtypes of neuropathic facial pain and differentiate them from healthy controls. We conducted a retrospective analysis of diffusion tensor and T1-weighted imaging data using random forest and logistic regression AI models on 371 adults with trigeminal pain (265 classical trigeminal neuralgia (CTN), 106 trigeminal neuropathic pain (TNP)) and 108 healthy controls (HC). These models distinguished CTN from HC with up to 95% accuracy, and TNP from HC with up to 91% accuracy. Both classifiers identified gray and white matter-based predictive metrics (gray matter thickness, surface area, and volume; white matter diffusivity metrics) that significantly differed across groups. Classification of TNP and CTN did not show significant accuracy (51%) but highlighted two structures that differed between pain groups-the insula and orbitofrontal cortex. Our work demonstrates that AI models with brain imaging data alone can differentiate neuropathic facial pain subtypes from healthy data and identify regional structural indicates of pain.

Indexed as

Artificial IntelligenceNeuralgiaAdultBrainFacial PainHumansNeuroimagingRetrospective Studies

Identifiers

PMID37400574
PMCPMC10318015
OpenAlexW4382939216

What OpenQuestion holds

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LicenceCC BY
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.