基于深度学习的监控技术,用于实时检测静脉注射药物袋的状态
Young Jun Hwang1, Gun Ho Kim2, Min Jae Kim1
1Department of Biomedical Engineering, School of Medicine, Pusan National University, Yangsan, Korea.
Biomedical engineering letters
|June 26, 2023
概括
这项研究引入了一个智能IV杆,使用人工智能监测药物,减少错误的药物管理等错误,并确保患者安全,尽量减少额外的护理工作. 该系统准确地识别药物和剩余液体水平,增强医院护理.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 静脉注射 (IV) 药物管理容易产生错误,包括过量剂量,剂量不足和错误识别,从而危及患者的安全.
- 现有的监测方法往往会增加护理工作量,限制它们的长期应用.
研究的目的:
- 开发和评估一个智能IV杆系统,用于实时监测IV药物状态.
- 为了减少与静脉注射相关的事故,并提高患者的安全,医疗保健专业人员的额外工作负担最小化.
主要方法:
- 设计了一个智能IV杆系统,整合了12个摄像头,一个代码扫描仪和四个控制器.
- 实施了两个深度学习模型 (CNN-1用于摄像机选择,CNN-2用于液体残留监测) 和三个药物残留估计方程.
- 该系统监控患者/药物识别和剩余液体体积,最多可用于四种静脉注射药物.
主要成果:
- 该系统在识别代码检查和CNN-1分类方面实现了100%的准确性.
- 在液体残留物监测中,CNN-2的平均精度为0.94.
- 报警设置 (20-40毫升) 的平均错误率在不同的袋子大小 (100-1000毫升) 中处于可接受范围内.
结论:
- 基于AI的智能IV杆原型显示了减少IV药物错误的巨大潜力.
- 这项技术可以通过提供持续的,低工作负载的输液状态监测来提高医院患者的安全性.
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