Artificial intelligence in epilepsy phenotyping

• Published in: Epilepsia (Copenhagen)
• Main Authors: Knight, Andrew, Gschwind, Tilo, Galer, Peter, Worrell, Gregory A., Litt, Brian, Soltesz, Ivan, Beniczky, Sándor
• Format: Journal Article
• Language: English
• Published: United States 20.11.2023
• Subjects: EEG; artificial intelligence; seizure
• ISSN: 0013-9580; 1528-1167; 1528-1167
• DOI: 10.1111/epi.17833

Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro‐clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI‐based automated and semi‐automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high‐frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.