Kerr Wesley, Acosta Sandra, Kwan Patrick, Worrell Gregory, Mikati Mohamad A
Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Department of Biomedical Engineering, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Epilepsy Curr. 2024 Mar 31:15357597241238526. doi: 10.1177/15357597241238526.
Artificial intelligence, machine learning, and deep learning are increasingly being used in all medical fields including for epilepsy research and clinical care. Already there have been resultant cutting-edge applications in both the clinical and research arenas of epileptology. Because there is a need to disseminate knowledge about these approaches, how to use them, their advantages, and their potential limitations, the goal of the 2023 Merritt-Putnam Symposium and of this synopsis review of that symposium has been to present the background and state of the art and then to draw conclusions on current and future applications of these approaches through the following: (1) Initially provide an explanation of the fundamental principles of artificial intelligence, machine learning, and deep learning. These are presented in the first section of this review by Dr Wesley Kerr. (2) Provide insights into their cutting-edge applications in screening for medications in neural organoids, in general, and for epilepsy in particular. These are presented by Dr Sandra Acosta. (3) Provide insights into how artificial intelligence approaches can predict clinical response to medication treatments. These are presented by Dr Patrick Kwan. (4) Finally, provide insights into the expanding applications to the detection and analysis of EEG signals in intensive care, epilepsy monitoring unit, and intracranial monitoring situations, as presented below by Dr Gregory Worrell. The expectation is that, in the coming decade and beyond, the increasing use of the above approaches will transform epilepsy research and care and supplement, but not replace, the diligent work of epilepsy clinicians and researchers.
人工智能、机器学习和深度学习在包括癫痫研究与临床护理在内的所有医学领域正得到越来越广泛的应用。在癫痫学的临床和研究领域已经出现了由此产生的前沿应用。由于有必要传播关于这些方法、其使用方式、优点及潜在局限性的知识,2023年梅里特 - 普特南研讨会以及本研讨会综述的目标是介绍背景和技术现状,然后通过以下方式就这些方法的当前和未来应用得出结论:(1)首先解释人工智能、机器学习和深度学习的基本原理。韦斯利·克尔博士在本综述的第一部分介绍了这些内容。(2)深入了解它们在一般神经类器官药物筛选,特别是癫痫药物筛选中的前沿应用。桑德拉·阿科斯塔博士介绍了这些内容。(3)深入了解人工智能方法如何预测药物治疗的临床反应。帕特里克·关博士介绍了这些内容。(4)最后,深入了解在重症监护、癫痫监测单元和颅内监测情况下脑电图信号检测与分析方面不断扩展的应用,如下由格雷戈里·沃雷尔博士介绍。预计在未来十年及以后,上述方法的更多使用将改变癫痫研究与护理,并补充但不会取代癫痫临床医生和研究人员的辛勤工作。