Artificial Intelligence in Epilepsy: A Systemic Review

Area of Science:

• Neurology
• Medical Informatics
• Artificial Intelligence

Background:

• Epilepsy diagnosis and management present significant clinical challenges.
• Surgical interventions offer benefits but often involve lengthy treatment pathways.
• Exploring advanced computational methods is crucial for enhancing patient care.

Purpose of the Study:

• To systematically review the application of artificial intelligence (AI) and machine learning (ML) in epilepsy diagnosis and treatment.
• To assess the potential of ML in predicting treatment response and surgical outcomes.
• To identify current limitations and future directions for ML in epilepsy management.

Main Methods:

• Comprehensive literature search across major scientific databases (PubMed, Scopus, Web of Science, etc.) from 2015-2022.
• Inclusion of 36 original English-language studies focusing on ML for epilepsy.
• Categorization of studies into diagnosis, treatment outcome, surgical candidate identification, and surgical result prediction.

Main Results:

• ML models utilized diverse data (symptoms, brain scans) and algorithms (SVM, CNN).
• Some models demonstrated high predictive accuracy (AUC up to 0.99).
• Key limitations include small sample sizes and insufficient independent validation across studies.

Conclusions:

• Machine learning holds significant potential for advancing epilepsy care.
• Current research is constrained by data limitations and the need for robust validation.
• Future efforts should focus on large-scale collaborative research and long-term outcome data for clinical integration.