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相关实验视频

Updated: Jan 7, 2026

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation
10:25

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Published on: September 2, 2025

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一个基于深度学习的人机协作导航框架,用于血管干预手术.

Yan Zhao1, Hui Li1, Runbo Liu1

  • 1School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China.

The international journal of medical robotics + computer assisted surgery : MRCAS
|December 30, 2025
PubMed
概括

这项研究介绍了自主血管干预手术 (VIS) 的深度学习框架. 该系统在导管行动决策中达到93.75%的准确性,提高了手术的有效性和安全性.

关键词:
深度学习是一种深度学习.人类机器人协作导航协作导航手术导航,手术导航,手术导航,手术导航,手术导航血管干预手术机器人手术机器人

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 医疗技术 医疗技术 医学技术

背景情况:

  • 自主机器人手术有可能改善患者的治疗结果.
  • 血管干预手术 (VIS) 为自主执行带来了独特的挑战,原因是动态的手术状态和软组织.

研究的目的:

  • 开发一个端到端的深度学习框架,用于人机协作导航在VIS.
  • 允许在VIS中自主执行预先计划的任务.

主要方法:

  • 一个新的框架,利用手术生成对抗网络 (GANs) 实时进行导管路径规划.
  • 一个基于卷积神经网络 (CNN) 的动作估计器用于非线性映射.
  • 一个基于信任的人机器人导航的共享控制模型.

主要成果:

  • 该系统在实验中实现了93.75%的导管动作决策准确度.
  • 与现有方法相比,已经证明了更好的外科疗效和安全性.
  • 网络在模拟导管环境中的自建数据集上进行了训练.

结论:

  • 拟议的框架为实现自主VIS提供了一条可行的途径.
  • 突出了深度学习在克服复杂外科手术过程中的挑战方面的潜力.