用机器学习建模磁控软体吸力器械，实现亚毫米级实时形变预测。
Learning-Based Modeling of a Magnetically Steerable Soft Suction Device for Endoscopic Endonasal Interventions
- 通过贝塞尔控制点建模器械形变，将磁控参数映射为低维几何表示。
- 随机森林模型误差仅0.064毫米，显著优于神经网络。
- 适合需高精度控制的微创神经外科手术场景。
本文提出一种基于学习的建模框架，用于磁控可弯曲软体吸力装置在内镜经鼻脑瘤切除术中的应用。该装置微型化（外径4毫米，内径2毫米，长40毫米），采用生物相容性SIL 30材料3D打印，并集成嵌入式光纤布拉格光栅（FBG）传感器实现形变实时反馈。形变由四个贝塞尔控制点表示，形成紧凑的几何表达。基于5,097组实验数据训练了数据驱动模型，学习磁场均值（0-14 mT）、频率（0.2-1.0 Hz）及垂直尖端距离（90-100 mm）到控制点的映射关系。对比神经网络（NN）与随机森林（RF）后发现，RF模型表现更优，控制点预测均方根误差为0.087毫米，形变重建误差达0.064毫米。特征重要性分析表明，磁场分量主要影响远端控制点，而频率与距离则影响基部构型。区别于以往通用机器学习方法，本框架首次将磁驱动输入直接关联至贝塞尔控制点，建立可解释、低维的形变表示。该融合磁场表征、嵌入式FBG传感与贝塞尔学习的统一策略，可扩展至其他磁控连续体机器人。通过实现亚毫米级形变预测与实时推理，推动磁控软体器械在微创神经外科中的智能控制发展。
原文摘要 · Abstract (English)
This paper introduces a learning-based modeling framework for a magnetically steerable soft suction device designed for endoscopic endonasal brain tumor resection. The device is miniaturized (4 mm outer diameter, 2 mm inner diameter, 40 mm length), 3D printed using biocompatible SIL 30 material, and integrates embedded Fiber Bragg Grating (FBG) sensors for real-time shape feedback. Shape reconstruction is represented using four Bezier control points, providing a compact representation of deformation. A data-driven model was trained on 5,097 experimental samples to learn the mapping from magnetic field parameters (magnitude: 0-14 mT, frequency: 0.2-1.0 Hz, vertical tip distances: 90-100 mm) to Bezier control points defining the robot's 3D shape. Both Neural Network (NN) and Random Forest (RF) architectures were compared. The RF model outperformed the NN, achieving a mean RMSE of 0.087 mm in control point prediction and 0.064 mm in shape reconstruction error. Feature importance analysis revealed that magnetic field components predominantly influence distal control points, while frequency and distance affect the base configuration. Unlike prior studies applying general machine learning to soft robotic data, this framework introduces a new paradigm linking magnetic actuation inputs directly to geometric Bezier control points, creating an interpretable, low-dimensional deformation representation. This integration of magnetic field characterization, embedded FBG sensing, and Bezier-based learning provides a unified strategy extensible to other magnetically actuated continuum robots. By enabling sub-millimeter shape prediction and real-time inference, this work advances intelligent control of magnetically actuated soft robotic tools in minimally invasive neurosurgery.
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