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基于机器学习的生成框架,用于反向设计穿孔针偏斜曲线.

Yaozong Huang1, Fan Zhang1, Fanyang Zhang1

  • 1Laboratory of Intelligent Control and Robotics, Shanghai University of Engineering Science, Shanghai, People's Republic of China.

Computer methods in biomechanics and biomedical engineering
|December 20, 2025
PubMed
概括

本研究提出了一种机器学习框架,用于设计精确导航软组织的医疗针. 该生成模型优化了针尖参数以获得准确的偏移曲线,提高了外科手术的精度.

关键词:
针头尖的偏移偏移是针头的偏移.组合学习组合学习有限元分析是有限元分析.生成型机器学习是一种机器学习.逆向设计是一种逆向设计.医疗器械 医疗器械 医疗器械

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科学领域:

  • 医疗器械设计 医疗器械设计
  • 机器学习应用程序 机器学习应用程序
  • 生物医学工程 生物医学工程

背景情况:

  • 医学针在软组织中的精确导航仍然是一个重大挑战.
  • 目前设计针轨迹的方法缺乏适应性和精度.

研究的目的:

  • 开发一个生成的机器学习框架,用于反向设计穿孔针曲线.
  • 为了获得最佳的针尖设计参数,以便在软组织中精确导航.

主要方法:

  • 一个具有逆向预测和前向验证组件的生成机器学习框架.
  • 一种基于欧勒-伯努利束理论的针-组织相互作用物理模型.
  • 通过参数抽样和集体学习的应用生成10,000个数据集.

主要成果:

  • 曲线分类器实现了F1分数从0.945到0.969;偏向曲线回归器显示R2为0.9981的0.0008毫米平均误差.
  • 预测设计参数的平均相对误差在5%以内,使用集体学习减少了4.3%.
  • 经过验证的最佳设计显示出与目标轨迹 (NRMSE 1.15903E-4,偏差 ±0.3 mm) 的良好一致.

结论:

  • 该框架为精确的医疗针头尖端设计提供了有效的解决方案.
  • 这项研究为智能医疗器械设计建立了新的范式.
  • 开发的模型能够准确地预测和优化针偏移曲线,用于目标组织导航.