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Remote Magnetic Navigation for Accurate, Real-time Catheter Positioning and Ablation in Cardiac Electrophysiology Procedures
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基于机器学习的多模式力估计,用于可引导的废除导管.

E Arefinia1, J Jayender2, R V Patel1

  • 1Department of Electrical and Computer Engineering, Western University, London, ON, Canada, and Canadian Surgical Technologies and Advanced Robotics (CSTAR), University Hospital, LHSC, London, ON, Canada.

IEEE transactions on medical robotics and bionics
|September 16, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种基于人工智能的新系统,用于估计心脏剥离手术期间的导管接触力. 多模式方法显著提高了准确性,提高了心房的治疗患者的安全性.

关键词:
美国有线电视新闻网 (CNN)通过心脏切除术 (Cardiac Ablation) 进行心脏切除.的 LSTMs 的使用.多模式力量估计多模式力量估计光学流量估计估计器在RNN中,RNN是指RNN.形状提取 形状提取可指向的废除导管.

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科学领域:

  • 心血管医学 心血管医学
  • 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:

  • 导管式心脏移除对于治疗心房动 (AF) 是至关重要的.
  • 在导管和心脏组织之间精确的接触力测量对于有效创建病变和程序成功至关重要.
  • 目前的方法可能缺乏精度,需要改进的传感技术.

研究的目的:

  • 开发和验证一种新的多式联接力估计器,用于导管式心脏切除.
  • 利用深度学习,特别是卷积神经网络 (CNN) 和循环神经网络 (RNN),进行增强的力估计.
  • 通过模拟光学和现实数据采集来评估估计器的性能.

主要方法:

  • 采用视频数据的导管形状和光学流的多模式方法.
  • 通过从图像和光流模式的转移学习提取特征.
  • 长短期记忆网络 (LSTM) 与记忆融合网络 (MFN) 的集成,以捕捉时间依赖和歇斯底里.
  • 具有晚期融合技术的空间和时间网络架构 (LSTM,变压器解码器,GRU的连锁).

主要成果:

  • 拟议的多式联运网络的平均绝对误差仅为总量中的2.84%.
  • 性能优于单模网络和基本的晚期聚变连接.
  • 在现实的条件下收集的数据证明了估计器的实用性.

结论:

  • 开发的基于人工智能的多模式接触力估计器非常准确和实用,用于心脏剥离.
  • 这种无传感器的方法比现有的方法有了显著的改进.
  • 这些发现突显了多式联络深度学习在推进微创心脏手术方面的潜力.