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使用3D卷积神经网络的手术室基于视频的自动手部卫生检测.

Minjee Kim1,2, Joonmyeong Choi3, Jun-Young Jo4

  • 1Department of Biomedical Engineering, University of Ulsan College of Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, 05505, Republic of Korea.

Journal of clinical monitoring and computing
|June 19, 2024
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概括

使用卷积神经网络 (CNN) 的深度学习模型可以自动检测手部卫生操作在手术室. 这项技术为持续监测提供了一种具有成本效益的解决方案,以预防医院感染.

关键词:
计算机视觉 计算机视觉 计算机视觉深度学习是一种深度学习.关于手的卫生 关于手的卫生手部卫生检测器 检测手部卫生在医院获得的感染.

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科学领域:

  • 医学技术 医学技术 医学技术
  • 医疗保健中的人工智能
  • 控制感染的方法

背景情况:

  • 医院感染 (HAI) 是手术室的一个重要问题.
  • 麻醉人员的有效手部卫生对于预防HAI至关重要.
  • 目前用于监测手部卫生遵守的方法通常是低效的或劳动密集型的.

研究的目的:

  • 开发和评估一种深度学习算法,用于自动检测基于酒精的手部卫生行为.
  • 评估使用视频监控和人工智能用于在手术室中持续监测手部卫生的可行性.
  • 提高手部卫生监督的效率和成本效益.

主要方法:

  • 利用了一种连续的深度学习方法,将2D和3D卷积神经网络 (CNN) 结合起来.
  • 采用2D CNN用于视频中的多人检测,3D CNN用于分类手部卫生行动.
  • 集成的光流作为一个额外的输入模式,以提高检测准确度.
  • 从手术室设置中收集和模拟的视频数据.

主要成果:

  • 开发的算法在检测手部卫生行为方面取得了高性能.
  • 评估显示,对二进制分类的准确度为0.88,灵敏度为0.78,特异性为0.93,AUC为0.91.
  • 深度学习模型证明了从视频数据中识别手部卫生事件的有效性.

结论:

  • 基于3D CNN的深度学习算法可以可靠地检测麻醉人员执行的手部卫生操作.
  • 这种人工智能驱动的方法有可能在持续的,具有成本效益的监测中实际临床应用.
  • 自动监测手部卫生可以有助于减少在医院获得的感染在操作环境.