{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T21:08:33Z","timestamp":1793394513575,"version":"4.5.0"},"reference-count":109,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:p>Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.<\/jats:p>","DOI":"10.3389\/fnins.2021.684825","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T01:32:24Z","timestamp":1624325544000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":52,"title":["Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review"],"prefix":"10.3389","volume":"15","author":[{"given":"Daichi","family":"Sone","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iman","family":"Beheshti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,6,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1111\/epi.16333","article-title":"Machine learning applications in epilepsy.","volume":"60","author":"Abbasi","year":"2019","journal-title":"Epilepsia"},{"key":"B2","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.nicl.2016.12.030","article-title":"Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy.","volume":"14","author":"Adler","year":"2017","journal-title":"Neuroimage Clin."},{"key":"B3","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.yebeh.2015.04.055","article-title":"Cortical feature analysis and machine learning improves detection of \u201cMRI-negative\u201d focal cortical dysplasia.","volume":"48","author":"Ahmed","year":"2015","journal-title":"Epilepsy Behav."},{"key":"B4","doi-asserted-by":"publisher","first-page":"102506","DOI":"10.1016\/j.nicl.2020.102506","article-title":"Topographic brain tumor anatomy drives seizure risk and enables machine learning based prediction.","volume":"28","author":"Akeret","year":"2020","journal-title":"Neuroimage Clin."},{"key":"B5","doi-asserted-by":"publisher","first-page":"101618","DOI":"10.1016\/j.media.2019.101618","article-title":"Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: application to epilepsy lesion screening.","volume":"60","author":"Alaverdyan","year":"2020","journal-title":"Med. 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