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Neural Eng."],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Machine learning (ML) and deep learning (DL) models are increasingly being explored for individualised epilepsy risk prediction after a first unprovoked seizure (UFS) and after acute brain insults such as stroke or traumatic brain injury. We systematically evaluated their predictive performance, input modalities, validation strategies, methodological quality, and translational readiness across these two clinical pathways.\n                    <jats:italic>Approach.<\/jats:italic>\n                    PubMed, Scopus, IEEE Xplore, and Web of Science were searched for English-language human studies published between January 2005 and October 2025. Eligible studies used ML or DL to predict seizure recurrence after UFS or epilepsy development after acute brain insult using clinical, neuroimaging, electrophysiological, electronic-health-record, or multimodal data. Two reviewers performed blinded duplicate screening, followed by duplicate data extraction using a CHARMS-aligned form. Risk of bias and applicability were independently assessed using PROBAST+AI across the Participants, Predictors, Outcome, and Analysis domains.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Thirteen studies met the eligibility criteria: six addressed UFS and seven addressed post-insult epilepsy. Reported AUCs for the best-performing models ranged from 0.60 to 0.93, with the highest discrimination observed in models using high-dimensional neuroimaging, unstructured clinical text, or multimodal data. These inputs included MRI morphometric asymmetry, clinical free text, EEG, diffusion MRI, resting-state fMRI, and multimodal fusion. In the three studies that directly compared modality combinations, multimodal models improved AUC by approximately 0.04\u20130.10 over the best single-modality counterpart. Model credibility was strongest when independent validation, transparent feature handling, and calibration assessment were reported.\n                    <jats:italic>Significance.<\/jats:italic>\n                    ML\/DL approaches show clear potential for earlier, individualised epilepsy risk stratification, particularly when complementary clinical, electrophysiological, and neuroimaging data are integrated. Future studies should prioritise prospective multi-site validation, standardised EEG\/MRI data structures, transparent multi-metric reporting, and reproducible model documentation aligned with TRIPOD+AI and PROBAST+AI.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ae8eb1","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T22:51:33Z","timestamp":1784760693000},"page":"041002","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning approaches for prediction of epilepsy risk across clinical pathways: a systematic review"],"prefix":"10.1088","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0741-3032","authenticated-orcid":true,"given":"Amr M","family":"Omar","sequence":"first","affiliation":[{"name":"Queen\u2019s University"},{"name":"Centre for Neuroscience Studies, Queen\u2019s University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6158-3260","authenticated-orcid":true,"given":"Antonina","family":"Omisade","sequence":"additional","affiliation":[{"name":"Acquired Brain Injury (Epilepsy Program), Nova Scotia Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7362-109X","authenticated-orcid":false,"given":"Jason P","family":"Gallivan","sequence":"additional","affiliation":[{"name":"Queen\u2019s University"},{"name":"Centre for Neuroscience Studies, Queen\u2019s University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9395-1478","authenticated-orcid":true,"given":"Gavin P","family":"Winston","sequence":"additional","affiliation":[{"name":"Queen\u2019s University"},{"name":"Centre for Neuroscience Studies, Queen\u2019s University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"jneae8eb1bib1","article-title":"Epilepsy: key facts","author":"World Health Organization","year":"2024","type":"other"},{"key":"jneae8eb1bib2","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1016\/j.ncl.2016.06.015","type":"journal-article","article-title":"The epidemiology of global epilepsy","volume":"34","author":"Singh","year":"2016","journal-title":"Neurol. 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