Department 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.
Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, 05505, Republic of Korea.
J Clin Monit Comput. 2024 Oct;38(5):1187-1197. doi: 10.1007/s10877-024-01179-6. Epub 2024 Jun 19.
Hand hygiene among anesthesia personnel is important to prevent hospital-acquired infections in operating rooms; however, an efficient monitoring system remains elusive. In this study, we leverage a deep learning approach based on operating room videos to detect alcohol-based hand hygiene actions of anesthesia providers. Videos were collected over a period of four months from November, 2018 to February, 2019, at a single operating room. Additional data was simulated and added to it. The proposed algorithm utilized a two-dimensional (2D) and three-dimensional (3D) convolutional neural networks (CNNs), sequentially. First, multi-person of the anesthesia personnel appearing in the target OR video were detected per image frame using the pre-trained 2D CNNs. Following this, each image frame detection of multi-person was linked and transmitted to a 3D CNNs to classify hand hygiene action. Optical flow was calculated and utilized as an additional input modality. Accuracy, sensitivity and specificity were evaluated hand hygiene detection. Evaluations of the binary classification of hand-hygiene actions revealed an accuracy of 0.88, a sensitivity of 0.78, a specificity of 0.93, and an area under the operating curve (AUC) of 0.91. A 3D CNN-based algorithm was developed for the detection of hand hygiene action. The deep learning approach has the potential to be applied in practical clinical scenarios providing continuous surveillance in a cost-effective way.
麻醉人员的手部卫生对于预防手术室医院获得性感染非常重要；然而，高效的监测系统仍然难以实现。在这项研究中，我们利用基于手术室视频的深度学习方法来检测麻醉提供者的基于酒精的手部卫生操作。从 2018 年 11 月到 2019 年 2 月，在一个手术室中收集了四个月的视频。还模拟并添加了其他数据。所提出的算法依次使用二维（2D）和三维（3D）卷积神经网络（CNNs）。首先，使用预先训练的 2D CNN 对目标 OR 视频中出现的麻醉人员的多个人进行每帧图像的检测。此后，将多个人的每个图像帧检测链接并传输到 3D CNN 以对手部卫生动作进行分类。计算光流并将其用作附加输入模式。对手卫生检测的准确性、敏感性和特异性进行了评估。手部卫生动作的二进制分类评估显示，准确性为 0.88，敏感性为 0.78，特异性为 0.93，操作曲线下面积（AUC）为 0.91。已经开发了一种基于 3D CNN 的算法来检测手部卫生动作。深度学习方法有可能以具有成本效益的方式应用于实际临床场景，提供持续监控。
