通过监督深度学习技术检测到的平方流辅助通风过程中的流量饥饿
Candelaria de Haro1,2, Verónica Santos-Pulpón3,4, Irene Telías5,6,7
1Critical Care Department, Parc Taulí Hospital Universitari, Institut d'Investigació I Innovació Parc Taulí (I3PT-CERCA),, Carrer Parc Taulí, 1, 08208, Sabadell, Spain. cdeharo@tauli.cat.
Critical care (London, England)
|March 15, 2024
概括
人工智能通过分析气道压力,准确地识别了流量饥饿,患者-通风器异步. 这种人工智能工具可以帮助实时最大限度地减少未被识别的不恰当的患者 - 呼吸机交互的情况.
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:
- 流量饥饿是一种患者-通风器异步,空气流无法满足需求,通常通过波形分析来确定.
- 目前的临床诊断具有挑战性,容易导致诊断不足,这凸显了对自动检测的需求.
- 人工智能为改善机械通风过程中气道压力变形的识别提供了机会.
研究的目的:
- 开发一个监督的人工智能算法,用于检测气道压力变形.
- 为了识别特定类型的患者-呼吸器异步在方流辅助通风和患者触发的呼吸.
主要方法:
- 这是一项多中心的观察性研究,包括成人危急病患者在机械通风上24小时以上.
- 五位重症监护专家将气道压力变形严重程度作为参考标准.
- 卷积神经网络 (CNN) 和循环神经网络 (RNN) 模型被训练并对准确性,精度,回忆和F1分数进行评估.
主要成果:
- 对6428次呼吸的分析显示,34%的呼吸道有严重的压力变形.
- 在识别气道压力变形方面,RNN算法获得了87.9%的准确性,超过了CNN (86.8%).
- 严重的变形与高的吸气力度有关 (ΔPes>>>10 cmH2O在74.4%的呼吸中).
结论:
- 循环神经网络模型在识别气道压力变形时表现出色,这种变形是由流量饥饿引起的.
- 这种人工智能工具有可能作为一个实时的,24小时的床边监控系统.
- 实施这项技术可以帮助最大限度地减少未被识别的,不适当的患者 - 呼吸机交互的时期.
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