一种基于图像引导机器人辅助干预系统的双通道深度学习网络的呼吸信号监测方法
Xiaodong Wang1,2, Jianjun Zhu2,3, Yong Wang4
1School of Automation, Nanjing University of Information Science and Technology, Nanjing, China.
这项研究引入了一种新的图像引导机器人辅助干预 (IGRI) 系统,该系统使用深度学习进行实时呼吸监测. 这通过改善机器人手术中的呼吸门来提高手术安全性.
科学领域:
- 医疗机器人 医疗机器人
- 以图像为指导的干预措施
- 人工智能在医学中的应用
背景情况:
- 使用图像引导机器人辅助干预 (IGRI) 系统进行皮穿刺手术,容易受到呼吸运动器件的影响.
- 在干预期间,患者的疼痛和痛苦可能会导致呼吸节律发生显著的干扰.
研究的目的:
- 为IGRI系统开发一个集成的呼吸信号监测模块.
- 通过解决呼吸运动,提高机器人辅助干预的安全性和准确性.
主要方法:
- 开发一个IGRI系统,包括一个编码结构光摄像头和一个双眼镜摄像头.
- 实现双通道深度学习网络,结合卷积长短期记忆 (ConvLSTM) 和点长短期记忆 (PointLSTM) 模块.
- 实时呼吸信号监测和呼吸模式的分类.
主要成果:
- 与单个LSTM模块相比,拟议的双通道深度学习网络在准确性,精度,回忆和F1分数方面表现优异,用于呼吸模式分类.
- 在内部数据集上的实验验验证了集成网络的有效性.
结论:
- 一个新的呼吸信号监测模块成功地用于IGRI系统,使用双眼相机和双通道深度学习网络.
- 综合模块为在IGRI系统中应用呼吸门技术奠定了基础,从而通过增强的监控和安全机制提高了手术安全性.
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