Knight Andrew, Gschwind Tilo, Galer Peter, Worrell Gregory A, Litt Brian, Soltesz Ivan, Beniczky Sándor
Neuro Event Labs, Tampere University, Tampere, Finland.
Department of Neurosurgery, Stanford University, Stanford, California, USA.
Epilepsia. 2023 Nov 20. 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.
人工智能（AI）能够以前所未有的粒度和规模进行数据分析与整合。在此，我们回顾了将AI用于癫痫动物模型和患者的电临床表型分析的技术进展、挑战及未来前景。在转化研究中，AI模型可准确识别癫痫动物模型中的行为状态，从而确定神经活动与发作间期及发作期行为之间的相关性。基于AI的癫痫患者音频和视频记录的自动化及半自动化分析的临床应用，可大幅减少数据量，并可靠地检测和分类主要运动性癫痫发作。AI模型能够准确识别癫痫的脑电图生物标志物，如棘波、高频振荡和癫痫发作模式。整合脑电图、临床和行为数据的AI分析将有助于优化癫痫患者的治疗。
