在心肺复苏期间使用深度网络监测患者的呼吸道
Mahmoud Marhamati1, Behnam Dorry2, Shima Imannezhad3
1Department of Nursing, Esfarayen Faculty of Medical Science, Esfarayen, Iran.
Medical engineering & physics
|June 21, 2024
概括
这项研究使用深度学习在心肺复苏 (CPR) 期间自动检测患者的正确气道定位. 人工智能模型实现了高精度,改善了心肺复苏培训和患者安全.
科学领域:
- 医疗技术 医疗技术 医学技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 心肺复苏 (CPR) 需要精确的患者呼吸道管理,通常由人类教练监督.
- 精确的气道定位对于CPR期间的有效通风至关重要.
- 目前的监测方法依赖于人类的观察,这可能是主观的和资源密集的.
研究的目的:
- 开发和验证深度转移学习模型,用于在心肺复苏过程中自动检测正确和不正确的患者气道位置.
- 评估各种深度学习架构在不同通风方法期间识别气道位置方面的性能.
主要方法:
- 一个数据集由198个视频序列 (6-8s每个) 显示正确的/不正确的气道位置在口对口和Ambu Bag通风期间被策划.
- 六个预先训练的深度网络 (DarkNet19,EfficientNetB0,GoogleNet,MobileNet-v2,ResNet50,NasnetMobile) 在CPR数据集上进行了微调.
- 模型性能使用灵敏度,特异性和F-测量指标进行评估.
主要成果:
- 微调的模型在检测正确的气道位置方面表现出很高的准确性.
- 对于口口呼吸,最好的模型实现了98.8%的灵敏度,100%的特异性和97.2%的F测量.
- 对于Ambu Bag通风,最好的模型实现了100%的灵敏度,99.8%的特异性和99.7%的F测量.
结论:
- 深度转移学习提供了一种高度有效和准确的方法,用于在CPR中自动检测气道位置.
- 这项技术有可能通过确保正确的通风来加强心肺复苏培训,并改善患者的治疗结果.
- 开发的模型显示了在CPR程序期间实时监控和反的前景.
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