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Epilepsy and Seizures: Overview

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Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...

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Related Experiment Video

Updated: Jul 16, 2026

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

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Published on: October 2, 2014

Localizing the epileptogenic zone using deep learning and neuroimaging: A systematic review.

Petros Koutsouvelis¹, Sven J E Ermans², Leroy Volmer¹

¹Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.

|March 15, 2026

Summary

Deep learning (DL) shows promise for localizing the epileptogenic zone (EZ) in drug-resistant epilepsy. However, current studies face methodological limitations and bias, hindering clinical translation, necessitating standardized evaluation and advanced DL techniques.

Keywords:

Artificial intelligence Brain imaging Epilepsy Neurosurgery

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Area of Science:

Neuroimaging
Artificial Intelligence
Epilepsy Research

Background:

Deep learning (DL) offers potential for identifying the epileptogenic zone (EZ) to improve surgical outcomes in drug-resistant epilepsy.
Systematic review of DL applications in EZ localization from neuroimaging data.

Purpose of the Study:

Synthesize evidence on DL-assisted EZ localization.
Identify methodological trends, limitations, and future directions.
Bridge the gap between clinical translation and technological advancements.

Main Methods:

Systematic literature search of PubMed, Scopus, and Embase.
Assessment of study bias and applicability using PROBAST+AI tool.
Extraction of methodological details and performance metrics.

Main Results:

Thirty-six studies reviewed, primarily segmenting epileptogenic lesions with structural MRI.
Focal cortical dysplasia was the most common target; fully convolutional networks were the predominant DL architecture.
Two-thirds of studies had high risk of bias and limited clinical applicability; progress in fine-grained localization was moderate.

Conclusions:

Methodological limitations impede clinical translation of DL for EZ localization.
Recommendations provided to address identified limitations.
Future work should focus on standardized evaluation, uncertainty quantification, foundation models, and synthetic data.