ReviewFrontiers in neuroscience2021
Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review.
Review in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of machine learning approaches to predict seizure-onset zones in patients with drug-resistant epilepsy: a systematic review.Frontiers in neurology · 2025Pooled it
- Human biomarker navigator.iMeta · 2026Review
- Structural brain imaging biomarkers for predicting seizure recurrence after a first unprovoked seizure.Epilepsia open · 2026Article
- AI enabled decision support systems in epilepsy surgery a scoping review.Research square · 2026Article
- Enhancing seizure prediction using a DC-SA-EBiLSTM framework with self-attention mechanism.Frontiers in neuroscience · 2026Article
- Deep learning enabled decision support systems in epilepsy surgery: a scoping review.npj health systems · 2026Article
- Disease detection and classification in temporal lobe epilepsy: step-wise versus simultaneous AI decision models in a multisite neuroimaging study.Brain communications · 2026Article
- Automated detection of polymicrogyria in pediatric patients using deep learning.Scientific reports · 2025Article
- Language and Memory Network Alterations in Temporal Lobe Epilepsy: A Functional and Structural Connectivity Study.AJNR. American journal of neuroradiology · 2025Article
- Towards precision MRI biomarkers in epilepsy with normative modelling.Brain : a journal of neurology · 2025Review
- Automated detection of spreading depolarizations in electrocorticography.Scientific reports · 2025Article
- Estimated Disease Progression Trajectory of White Matter Disruption in Unilateral Temporal Lobe Epilepsy: A Data-Driven Machine Learning Approach.Brain sciences · 2024Article
- Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.BMC neurology · 2024Review
- White matter brain-age in diverse forms of epilepsy and interictal psychosis.Scientific reports · 2024Article
- Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.Sensors (Basel, Switzerland) · 2024Article
- Identifying epilepsy surgery referral candidates with natural language processing in an Australian context.Epilepsia open · 2024Article
- Role of machine learning in the management of epilepsy: a systematic review protocol.BMJ open · 2024Article
- Performance reserves in brain-imaging-based phenotype prediction.Cell reports · 2024Article
- Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy: A Proof-of-Principle Study.Neurology · 2023Article
- Extending artificial intelligence research in the clinical domain: a theoretical perspective.Annals of operations research · 2022Article
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Authors and funding
2 authors.
Funding
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Abstract
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.
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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.