Artefacts in continuous neuromonitoring

IF 42.7
Xuhang Chen, Stefan Yu Bögli, Ihsane Olakorede, Wenhao Xu, Erta Beqiri, Chenyu Tang, Luigi Giuseppe Occhipinti, Shuo Gao, Peter Smielewski
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Abstract

Continuous neuromonitoring is essential in neurocritical care units, providing real-time insights into dynamic cerebral physiology for patients with neurological conditions, such as stroke and neurotrauma. Multiple modalities such as intracranial pressure, arterial blood pressure and near-infrared spectroscopy enable high-resolution dynamic tracking of cerebral perfusion pressure, and related variables critical for life-saving decisions. However, inherent, commonly unavoidable artefacts obscure insights into patient states and complicate treatment decisions. In this Review, we explore primary sources of artefacts in continuous neuromonitoring modalities, including clinical procedure activities, patient-related physiology, technical equipment properties and environmental factors, and their impacts on data integrity and clinical implications. We discuss emerging artefact management strategies, including domain knowledge and data-driven methods to mitigate impact and enhance data reliability. Additionally, we identify key translational challenges, indications for neurosensor design, harmonization and future artificial intelligence pathways, highlighting the need for robust, automated, real-time artefact management to enable precise, individualized patient care. Continuous neuromonitoring is vital for guiding neurocritical care, yet artefacts frequently obscure cerebral physiological signals and hinder clinical decisions. In this Review, sources of artefacts and their clinical impact are examined, alongside emerging strategies — both knowledge-based and data-driven — for artefact mitigation, sensor design and artificial intelligence-enabled real-time monitoring in precision neurocare.

Abstract Image

连续神经监测中的伪影
连续的神经监测在神经危重症监护病房是必不可少的,为中风和神经创伤等神经系统疾病患者提供动态脑生理学的实时洞察。颅内压、动脉压和近红外光谱等多种模式可以实现脑灌注压的高分辨率动态跟踪,以及对救生决策至关重要的相关变量。然而,固有的,通常不可避免的人为因素模糊了对患者状态的了解,并使治疗决策复杂化。在这篇综述中,我们探讨了连续神经监测模式中伪影的主要来源,包括临床程序活动、患者相关生理、技术设备特性和环境因素,以及它们对数据完整性和临床意义的影响。我们讨论了新兴的人工制品管理策略,包括领域知识和数据驱动的方法,以减轻影响并提高数据可靠性。此外,我们还确定了关键的转化挑战、神经传感器设计的适应症、协调和未来的人工智能途径,强调了对强大、自动化、实时人工智能管理的需求,以实现精确、个性化的患者护理。连续的神经监测是至关重要的指导神经危重症护理,但人工制品往往模糊大脑生理信号,阻碍临床决策。在这篇综述中,人工产物的来源及其临床影响,以及新兴的策略——基于知识和数据驱动——用于人工产物缓解、传感器设计和精确神经护理中的人工智能实时监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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