通过深度学习网络,通过深度学习网络检测和定量分析在接受呼吸的婴儿中患者与呼吸器之间的相互作用
David Chong1, Gusztav Belteki2
1Neonatal Intensive Care Unit, The Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Pediatric research
|February 5, 2024
概括
患者与呼吸器之间的相互作用 (PVI) 在机械呼吸的新生儿中很常见,通常每次呼吸都会发生多次事件. 这项研究开发了用于自动检测PVI的深度学习模型,改进了对这些关键事件的分析.
科学领域:
- 新生儿重症监护的新生儿重症监护
- 呼吸系统生理学 呼吸系统生理学
- 医疗人工智能的人工智能
背景情况:
- 在新生儿机械通风中,患者与通风器的相互作用 (PVI) 是至关重要的.
- 缺乏标准化的PVI定义和大规模分析工具阻碍了研究.
- 之前的研究还没有完全解决新生儿PVI的患病率和同时发生的情况.
研究的目的:
- 确定新生儿中各种PVI的患病率和共发生率.
- 开发和验证用于自动PVI检测的计算模型.
- 适应成人PVI定义用于新生儿波形分析.
主要方法:
- 对23名新生儿进行机械通风的观察研究 (SIPPV-VG,SIMV-VG,PSV-VG).
- 每个新生儿进行500次呼吸的手动注释,以训练卷积神经网络 (CNN) 模型.
- 为七个常见的PVI开发二进制CNN分类器.
主要成果:
- 平均异步指数 (AI) 为52.5%.
- 呼吸工作是最常见的PVI (中位数为28.4%).
- 对于大多数PVI,CNN模型获得了高F1分数 (>0.9),而早期触发则为0.809.
结论:
- 在常规机械通风的新生儿中,PVI很常见,多个PVI经常同时发生.
- 开发的计算模型可实现新生儿PVI的自动检测和量化.
- 这些模型有助于进一步评估新生儿PVI临床意义.
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