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混合深度学习和基于模型的针形状预测.

Dimitri A Lezcano1, Yernar Zhetpissov1, Mariana C Bernardes2

  • 1Mechanical Engineering Department, Johns Hopkins University, MD 21201 USA.

IEEE sensors journal
|September 20, 2024
PubMed
概括
此摘要是机器生成的。

在前列腺癌干预期间,预测柔性针的轨迹至关重要. 这项研究引入了混合深度学习和基于模型的方法,用于准确的手术内针形状预测,改善患者的治疗结果.

关键词:
深度学习是一种深度学习.灵活的针针可以灵活使用.机器学习是机器学习.医疗器械 医疗器械是一种医疗器械.基于模型的基于模型的模型形状预测 形状预测

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科学领域:

  • 医疗机器人 医疗机器人
  • 手术导航 手术导航
  • 计算力学 计算力学 计算力学

背景情况:

  • 前列腺癌的最小侵入性手术通常使用灵活的角针来精确地控制方向.
  • 准确的内科预测针的轨迹是必要的,以避免敏感的结构和减少重新插入.
  • 在插入过程中预测针路径是具有挑战性的,因为不可预测的针-组织相互作用.

研究的目的:

  • 开发和验证一种新的混合方法,用于手术内预测针形状.
  • 为了利用一个李群理论模型来准确地表示针形状.
  • 引入一种自我监督的学习方法,用于在没有先前数据的情况下训练预测网络.

主要方法:

  • 开发了一种混合深度学习和基于模型的方法,整合了对针形状的验证Lie组模型.
  • 在网络培训中采用了一种新的自我监督学习方法,使数据稀缺的场景和转移学习成为可能.
  • 针形状预测被测试在C和S形插入的同质幻影组织中.

主要成果:

  • 混合方法实现了1.03mm的平均根-平方平均值预测误差.
  • 该系统在约3000个预测样本的数据集上进行了验证.
  • 预测了长度高达110毫米的针插入.

结论:

  • 介绍的混合方法在手术内预测针形状方面取得了重大进展.
  • 自主监督学习方法促进了强大的网络培训,即使数据有限.
  • 这项技术有可能提高前列腺癌微创手术的准确性和效率.