人工智能用于检测患者-呼吸机异步
Abdulhakim Tlimat1, Cosmo Fowler2, Sami Safadi3,4
1Drs. Tlimat, Bodduluri, Morris, and Bhatt are affiliated with the Division of Pulmonary, Allergy, and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Respiratory care
|April 3, 2025
概括
人工智能 (AI) 可以在侵入性机械通风过程中准确检测患者-通风器异步 (PVA). 这篇评论强调了人工智能.
科学领域:
- 关键护理医学 关键护理医学
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 患者-呼吸器异步 (PVA) 是侵入性机械通风的常见并发症.
- PVA与负面的临床结果有关,例如增加呼吸工作和巴罗创伤.
- 使用人工智能 (AI) 自动检测PVA是一个有前途的解决方案.
研究的目的:
- 审查和描述开发用于检测和量化PVA的AI模型.
- 评估AI在PVA检测中的性能和方法.
- 为PVA管理提供AI当前状态的概述.
主要方法:
- 进行了全面的文献搜索,确定了13项相关研究.
- 包括的研究涵盖了不同的临床环境和患者群体.
- 分析了机器学习和深度学习技术,以及性能指标.
主要成果:
- 人工智能模型在检测和量化PVA方面表现出很高的准确性.
- 在13个模型中,有10个模型的灵敏度和特异性大于0.9.9.
- 8个模型报告了各种异步类型的精度超过0.9.
结论:
- 人工智能模型显示,在侵入性机械通风中,PVA精确检测和量化具有显著的潜力.
- 目前的AI方法在不同患者群体和异步类型中有效.
- 未来的研究应该优先考虑在各种临床环境中验证AI模型.
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