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Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta-analysis of four clinical applications.

Overview

Authors: Judy Chen1, Ella Sahlas1, Yigu Zhou1, Natalie Chen2, Jim Xie2, Farhan Wadia1, Lorenzo Caciagli3, Aristides Hadjinicolaou4, Alexander G. Weil4, Roy W. Dudley5, Dewi V. Schrader6, Andrea Bernasconi7, Neda Bernasconi7, Boris C. Bernhardt1
ORCID iDs: Judy Chen, Ella Sahlas, Natalie Chen, Andrea Bernasconi, Neda Bernasconi, Boris C. Bernhardt
  1. Multimodal Imaging and Connectome Analysis Laboratory (MICA), McConnell Brain Imaging Centre and Centre for Excellence in Epilepsy at the Neuro, Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
  2. Temerty Faculty of Medicine University of Toronto Toronto Ontario Canada
  3. Department of Neurology, Inselspital, Sleep‐Wake‐Epilepsy Center, Bern University Hospital University of Bern Bern Switzerland
  4. Centre Hospitalier Universitaire Sainte‐Justine Université de Montréal Montreal Quebec Canada
  5. Montreal Children's Hospital McGill University Montreal Quebec Canada
  6. BC Children's Hospital University of British Columbia Vancouver British Columbia Canada
  7. Neuroimaging of Epilepsy Laboratory (NOEL), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
Institutions: Montreal Neurological Institute and Hospital (Canada); McGill University (Canada); University of Toronto (Canada); University of Bern (Switzerland); University Hospital of Bern (Switzerland); Centre Hospitalier Universitaire Sainte-Justine (Canada); Université de Montréal (Canada); Montreal Children's Hospital (Canada); University of British Columbia (Canada); BC Children's Hospital (Canada)
Journal: Epilepsia, volume 67, issue 8, pages 4194-4206
Dates: received 17 December 2025; accepted 31 March 2026; published online 5 June 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/epi.70254 · PMID 42246704 · PMCID PMC13525556 · OpenAlex W7163699585
Open access: hybrid, a free copy (OpenAlex)
Status: code on request
Categories: structural MRI / diffusion (modality), human (organism), epilepsy (population), clinical / translational (subfield)
Methods: Statistics, Machine learning
Keywords: artificial intelligence, epilepsy, machine learning, prognosis
MeSH: Artificial Intelligence*, Epilepsy*, Magnetic Resonance Imaging*, Clinical Decision-Making, Epilepsy, Temporal Lobe, Focal Cortical Dysplasia, Humans, Machine Learning (* major topic)
Topic: Epilepsy research and treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Vanier Scholarship; SickKids Foundation (NI17‐039); Natural Sciences and Engineering Research Council of Canada (NSERC Discovery‐1304413, NSERC Discovery‐243141, NSERC Discovery‐24779); Canadian Institutes of Health Research (CIHR MOP‐123520, CIHR MOP‐57840, FDN‐154298, PJT‐174995, PJT‐191853, PJT‐206196, PJT‐203761); Centre of Excellence in Epilepsy at the Neuro; Epilepsy Canada
Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

Objective: The application of artificial intelligence/machine learning (AI/ML) to magnetic resonance imaging (MRI) promises to enhance and support clinical decision‐making in epilepsy. However, there currently lacks an appropriate assessment of clinical utility and study rigor of current AI/ML‐driven models that are targeted toward supporting decision‐making within the clinical workup in epilepsy.

Methods: We systematically reviewed and examined the ability of current AI/ML‐driven models in MRI across four main applications within the clinical workup(s) for epilepsy: (1) diagnosis, (2) temporal lobe epilepsy lateralization, (3) lesion (focal cortical dysplasia) localization, and (4) postsurgical outcome prediction. We additionally assessed the risk of bias for each study model. Studies that employed AI/ML classification models trained on any MRI modality or sequence type were selected for qualitative assessment; those reporting accuracy rates were subsequently included in the meta‐analysis.

Results: Of 3227 searched articles, we identified 159 studies (n = 26 732 participants) for qualitative evaluation and 127 studies (n = 20 456) for inclusion in the meta‐analysis. Our results reveal that AI/ML on MRI could accurately distinguish epilepsy patients from healthy controls (overall accuracy = .87, 95% confidence interval [CI] = .85–.89), lateralize temporal lobe epilepsy (.90, 95% CI = .87–.93), localize epileptogenic lesions (.82, 95% CI = .74–.87), and predict postsurgical seizure freedom (.83, 95% CI = .78–.87). However, systematic assessment indicated a very high risk of bias in the literature, suggestive of overly optimistic performance estimates.

Significance: Although our results support overall high accuracy of AI/ML models in epilepsy diagnostics and prognostics, the literature remains susceptible to bias in participant recruitment and validation methods. Furthermore, most models were limited by study architecture that demands strict adherence to nonstandard, highly specific data acquisition and processing protocols that cannot be easily deployed for clinical implementation. We encourage closer interdisciplinary collaboration between clinical and scientific groups to improve validation studies, and outline suggested recommendations for future study design, analysis, and reporting.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Code

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The paper's code and data availability statement is in the Data section.

Tracing map

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Data

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Data availability statement

Template collection forms for data extraction, extracted data, and code are available upon request.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 4 keywords, 8 MeSH terms, 6 funders, 38 references.

Cite

This paper

Chen, J., Sahlas, E., Zhou, Y., Chen, N., Xie, J., Wadia, F., Caciagli, L., Hadjinicolaou, A., Weil, A. G., Dudley, R. W., Schrader, D. V., Bernasconi, A., Bernasconi, N., & Bernhardt, B. C. (2026). Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta-analysis of four clinical applications. Epilepsia, 67(8), 4194-4206. https://doi.org/10.1002/epi.70254

BibTeX

@article{chen2026use,
author = {Chen, Judy and Sahlas, Ella and Zhou, Yigu and Chen, Natalie and Xie, Jim and Wadia, Farhan and Caciagli, Lorenzo and Hadjinicolaou, Aristides and Weil, Alexander G. and Dudley, Roy W. and Schrader, Dewi V. and Bernasconi, Andrea and Bernasconi, Neda and Bernhardt, Boris C.},
title = {{Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta-analysis of four clinical applications}},
journal = {Epilepsia},
year = {2026},
month = jun,
volume = {67},
number = {8},
pages = {4194--4206},
publisher = {Wiley},
issn = {0013-9580},
doi = {10.1002/epi.70254},
url = {https://doi.org/10.1002/epi.70254},
pmid = {42246704},
pmcid = {PMC13525556}
}

RIS

TY - JOUR
AU - Chen, Judy
AU - Sahlas, Ella
AU - Zhou, Yigu
AU - Chen, Natalie
AU - Xie, Jim
AU - Wadia, Farhan
AU - Caciagli, Lorenzo
AU - Hadjinicolaou, Aristides
AU - Weil, Alexander G.
AU - Dudley, Roy W.
AU - Schrader, Dewi V.
AU - Bernasconi, Andrea
AU - Bernasconi, Neda
AU - Bernhardt, Boris C.
TI - Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta-analysis of four clinical applications
T2 - Epilepsia
J2 - Epilepsia
PY - 2026
DA - 2026/06/05
VL - 67
IS - 8
SP - 4194
EP - 4206
SN - 0013-9580
PB - Wiley
DO - 10.1002/epi.70254
UR - https://doi.org/10.1002/epi.70254
LA - en
ER -

CSL-JSON

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