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A Data-Driven Approach to Quantifying Immune States in Sepsis07:42

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相关实验视频

Updated: Jan 20, 2026

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结肠镜中的多模传感:一个数据驱动的方法.

Viola Del Bono1, Emma Capaldi1, Anushka Kelshiker2

  • 1Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA.

IEEE robotics and automation letters
|January 19, 2026
PubMed
概括

本研究介绍了一种机器学习 (ML) 框架,用于实时3D形状和力量估计,用于结肠镜软机器人袖子. 该系统在跟踪形状和力度方面实现了高精度,改善了最小侵入性手术.

关键词:
控制 控制 控制 控制 控制强力和触觉感应.软机器人的学习建模建模模型是什么软传感器和执行器 软传感器和执行器

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科学领域:

  • 机器人和自动化机器人与自动化
  • 医疗器械 医疗器械
  • 机器学习应用 机器学习应用

背景情况:

  • 软光学传感器为微创手术 (如结肠镜检查) 提供了潜力.
  • 复杂的多模式传感器响应对实时数据解释构成重大挑战.
  • 准确估计3D形状和接触力对于安全有效的结肠镜检查至关重要.

研究的目的:

  • 开发一种机器学习 (ML) 框架,用于实时估计3D形状和接触力,用于结肠镜软机器人袖子.
  • 创建一个自动化数据收集平台,以克服手动校准的局限性,并为ML生成大型数据集.
  • 调查基于ML的联系本地化能力,以提高空间意识.

主要方法:

  • 开发一个自动化平台,用于收集各种方向,曲率和接触力传感器数据.
  • 实施级联式ML架构,用于连续估计接触力和3D形状.
  • 训练ML模型来估计接触强度和位置在16个分布在袖子上的槽.

主要成果:

  • ML框架在形状和力估计方面实现了高精度,曲率的误差为4.7%,定向的误差为2.37%,力跟踪的误差为5.5%.
  • 接触力强度估计的误差在0.06N到0.31N之间.
  • 基于ML的接触本地化显示出有前途的空间分辨率,尽管距离很近,但8个槽的准确度超过80%.

结论:

  • 开发的ML框架可以准确地实时估计3D形状和结肠镜软机器人袖子的接触力.
  • 自动化数据收集平台促进了有效的ML模型培训和验证.
  • 该系统显示,通过增强的传感能力,可以显著提高微创结肠镜手术的安全性和有效性.