在机械通风中使用深度学习实时检测呼吸回路事件
Qian He1,2, Tingting Pan1, Haiyang Hou3
1Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
一个新的AI算法准确地检测呼吸系统循环事件,如机械通风期间的液体积聚和泄漏. 这种早期检测可能有助于预防需要呼吸辅助的患者出现严重并发症.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 呼吸系统护理 呼吸系统护理
背景情况:
- 机械通风对于呼吸支至关重要,但容易发生电路事件.
- 呼吸回路中的液体积累和泄漏增加了呼吸器相关事件 (VAE) 的风险.
- 这些事件往往没有被发现,延迟了必要的干预.
研究的目的:
- 开发和验证用于检测呼吸系统循环事件的AI算法.
- 在真实世界的临床数据中评估算法的准确性和概括性.
- 探索检测到的电路事件的临床意义和影响.
主要方法:
- 在48名患者的57,296次注释呼吸上训练了一个卷积神经网络.
- 该算法分析波形数据以检测流体积累和电路泄漏.
- 内部和外部验证数据集被用于评估性能.
主要成果:
- 人工智能在检测类似流体积累的模式方面取得了很高的准确性 (99.90%的内部,92.35%的外部).
- 泄漏检测的准确性超过99%.
- 91.7%的患者有循环事件,流体模式与增加的气道压力有关.
结论:
- 开发的AI算法从波形数据准确地和一般地检测呼吸回路事件.
- 实时检测能力为早期干预提供了潜力.
- 这项技术可能会减少与呼吸器相关的并发症,并改善患者的治疗结果.
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