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Related Concept Videos

Epilepsy and Seizures: Overview01:24

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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Seizures: Classification01:13

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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
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Related Experiment Video

Updated: Feb 28, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
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AI enabled decision support systems in epilepsy surgery a scoping review.

Kai Yu1, Shuang Zhou1, Meijia Song1

  • 1University of Minnesota.

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|February 27, 2026
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Summary

Artificial intelligence (AI) in epilepsy surgery shows limited real-world application. Most AI tools focus on pre-operative tasks, use small datasets, and lack external validation, hindering broad clinical adoption.

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Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Epilepsy surgery decision-making is complex.
  • Artificial intelligence (AI) offers potential support.
  • Evidence for AI implementation across the epilepsy surgery pathway is limited.

Purpose of the Study:

  • To map AI-enabled decision support systems in epilepsy surgery.
  • To characterize datasets, modeling, validation, and integration.
  • To identify gaps for scalable AI adoption.

Main Methods:

  • Scoping review of 145 studies (Jan 2018 - May 2025).
  • Analysis of AI systems across surgical stages and clinical tasks.
  • Characterization of datasets, modeling, validation, and workflow integration.

Main Results:

  • Literature concentrated on pre-operative AI; no intra-operative studies found.
  • Most studies used small, single-center, non-public datasets with supervised CNN models.
  • External validation and workflow integration were uncommon.

Conclusions:

  • Significant gaps exist in AI generalizability, workflow readiness, and equity.
  • Priorities include multi-center data, rigorous cross-site evaluation, and clinically meaningful endpoints.
  • Safe and scalable AI adoption requires addressing these limitations.