闭环和人工智能引导的心肺复苏使用实时生理信号:系统性审查和元分析
Farnia Ghasemi1, Mohammad Amin Gholami2, Niloofar Ghasemi3
1Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
BMC emergency medicine
|March 15, 2026
概括
人工智能 (AI) 在指导心肺复苏 (CPR) 方面表现有前途,通过评估信号和协助冲击决策. 然而,目前的证据是分散的,需要进一步的临床研究,以人工智能为导向的CPR验证.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 目前的心肺复苏 (CPR) 需要暂停,与高质量的CPR需要持续压缩形成鲜明对比.
- 由人工智能 (AI) 驱动的系统提供了实时CPR指导的潜力,包括节律和脉冲评估.
研究的目的:
- 在心肺复苏 (CPR) 或CPR主导分析期间实时指导中系统审查和元分析基于AI算法的性能.
- 评估AI在动物和人类心脏骤停场景中的有效性,用于闭环心肺复苏,冲击咨询和脉冲检测.
主要方法:
- 根据PRISMA 2020指南,对2010年至2025年的研究进行了系统的文献搜索.
- 包括专注于人工智能工具的研究,在动物或人类心脏骤停的CPR或CPR分析中提供实时指导.
- 使用双变的随机效应模型进行了元分析,以分类可冲击和不可冲击的实例,并在细分级报告发现.
主要成果:
- 14项研究符合纳入标准,包括动物闭环心肺复苏,人类冲击咨询算法和脉冲检测研究.
- 人类冲击咨询算法表现出高的聚合灵敏度 (0.93) 和特异性 (0.97),尽管存在显著的变化和偏差.
- 动物研究显示,人工智能控制器在 perfusion 标记物上有暂时的改善,而脉冲检测在压缩过程中不那么可靠.
结论:
- 人工智能系统可以在某些数据集中执行指导性心肺复苏,动物研究支持 perfusion-targeted control 的生理可行性.
- 人工智能引导的闭环CPR是一个有前途的概念,但仍然没有得到验证.
- 由于目前的证据碎片化,多样化和方法上有限,进一步的情节级临床研究至关重要.
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