Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Nat Rev Neurol. 2024 Jun;20(6):319-336. doi: 10.1038/s41582-024-00965-9. Epub 2024 May 8.
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.
人工智能(AI)正在迅速改变医疗保健领域,其在癫痫中的应用在过去十年中呈指数级增长。将 AI 整合到癫痫管理中有望彻底改变这种复杂疾病的诊断和治疗方式。然而,AI 向神经病学临床实践的转化尚未成功,这强调了需要考虑到迄今为止的进展,并评估 AI 的挑战和局限性。在这篇综述中,我们概述了使用各种数据模态开发的用于癫痫的 AI 应用:神经影像学、脑电图、电子健康记录、医疗设备和多模态数据集成。对于每一种模态,我们考虑了潜在的应用,包括癫痫发作的检测和预测、癫痫发作的定位、癫痫发作起始区的定位以及手术或神经刺激干预的评估,并回顾了迄今为止开发的 AI 工具的性能。我们还讨论了必须解决的方法学考虑因素和挑战,以成功地将 AI 整合到临床实践中。我们的目标是概述该领域的现状,并为未来利用 AI 提供指导,以改善癫痫的管理。