使用设备上的人工智能模型实时监测个人防护设备的遵守情况.
Yam Horesh1, Renana Oz Rokach1, Yotam Kolben1,2
1Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究引入了一种AI计算机视觉系统,用于实时监测医疗保健工作人员的个人防护设备 (PPE) 遵守情况. 设备上的系统提供了一个具有成本效益的解决方案,以提高合规性和提高患者安全.
科学领域:
- 医疗信息学 医疗信息学
- 计算机视觉 计算机视觉
- 感染控制 感染控制
背景情况:
- 个人防护设备 (PPE) 对于预防医疗机构感染至关重要.
- 目前用于监测个人防护设备合规性的方法,如教育和检查,往往是昂贵的,有效性有限.
- 一致和正确使用PPE对于其有效性至关重要.
研究的目的:
- 开发和评估一种基于人工智能的新型设备上计算机视觉系统,用于实时监测医疗保健工作者个人防护设备的遵守情况.
- 为了优化系统边缘计算使用树派5高效,无服务器操作.
- 在现实临床环境中评估系统的准确性和实际可行性.
主要方法:
- 创建了一个由7142张图像组成的自定义图像数据集,其中包括各种PPE组合 (口罩,手套,长袍).
- 单个PPE项目的二进制分类器使用轻量级的MobileNetV3模型进行训练.
- 该系统部署在Raspberry Pi 5上,用于边缘计算,并在心脏重症监护病房中进行评估,医务人员没有被看到.
主要成果:
- 人工智能模型在识别个人个人防护设备时取得了很高的准确性,从93%到97%不等.
- 所有个人防护设备项目的正确分类的整体准确率为85.58±0.82%.
- 实时评估表明,在临床环境中,每个项目准确率在87%至89%之间.
结论:
- 人工智能驱动的计算机视觉提供了一个有希望的,具有成本效益和高效的工具,用于改善医疗保健中的个人防护设备合规性.
- 开发的设备上系统证明了实时监控的实际可行性,提高了患者的安全性并降低了感染风险.
- 这项技术有可能显著加强医疗机构的感染预防策略.
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