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相关实验视频

Updated: Jul 15, 2025

Visualizing Motion Patterns in Acupuncture Manipulation
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Visualizing Motion Patterns in Acupuncture Manipulation

Published on: July 16, 2016

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基于先前信息和深度学习的背部针点定位方法.

Ying-Bin Liu1, Jian-Hua Qin1, Gui-Fen Zeng2

  • 1School of Mechanical and Control Engineering, Guilin University of Technology, Guilin, China.

International journal for numerical methods in biomedical engineering
|September 30, 2023
PubMed
概括
此摘要是机器生成的。

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这项研究引入了一种改进的深度学习方法,用于使用单一图像自动定位背部针点. 这种新的方法实现了90.12%的准确性,推进了医疗器械的智能针点选择.

科学领域:

  • 医疗机器人 医疗机器人
  • 计算解剖学的计算解剖学
  • 深度学习应用程序

背景情况:

  • 针点的准确性对于像针机器人这样的医疗设备的有效治疗至关重要.
  • 目前的智能针点选择技术缺乏成熟性,这给快速准确的自动定位带来了挑战.

研究的目的:

  • 开发一种用于自动背部针点定位的新方法.
  • 建立一个评估指数的针点位置准确性.
  • 为了提高用于机器人应用的针点识别的精度.

主要方法:

  • 为初步的针点定位提出了改进的Keypoint RCNN网络,其中包括道和空间注意力机制模块 (CBAM).
  • 采用后部中线定位方法来改进准确度.
  • 使用先前的信息进行了针点扩张和定位.

主要成果:

  • 最初的针点定位精度达到了87.32%.
  • 经过校正后,准确度提高了2.8%至90.12%.
  • 这项研究标志着深度学习的首次应用,用于使用单一图像自动定位针点.

结论:

关键词:
针点的位置位置.对针点定位的评估方法改进了关键点 RCNN 的提升.后部中线定位方法的定位方法.预先提供针点的信息.

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  • 拟议的方法在自动背部针点定位方面表现出高准确性.
  • 这一进步对提高针机器人的精度有重大影响.
  • 这项研究成功地将深度学习整合到自动化传统医学的关键任务中.