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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.
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Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) applications in epilepsy have grown significantly over the last decade.
  • AI integration in epilepsy management holds promise for revolutionizing diagnosis and treatment.
  • Clinical translation of AI in neurology faces challenges, necessitating a review of progress and limitations.

Purpose of the Study:

  • To provide an overview of current AI applications in epilepsy.
  • To assess the performance of AI tools across various data modalities.
  • To identify challenges and guide future AI integration in epilepsy management.

Main Methods:

  • Review of AI applications in epilepsy utilizing neuroimaging, electroencephalography (EEG), electronic health records (EHRs), and medical devices.
  • Analysis of AI tools for seizure detection, prediction, lateralization, and localization of seizure-onset zones.
  • Discussion of methodological considerations for clinical integration.

Main Results:

  • AI tools show potential in seizure detection, prediction, lateralization, and localization.
  • Performance of AI tools varies across different data modalities and applications.
  • Methodological challenges and limitations hinder widespread clinical adoption.

Conclusions:

  • AI offers significant potential to enhance epilepsy diagnosis and treatment.
  • Addressing technical and clinical challenges is crucial for successful AI implementation.
  • Future research should focus on robust validation and seamless integration into clinical workflows.