基于力传感和VAE-MLP的机器人辅助UBE手术的突破性识别

IF 2.1 3区 医学 Q2 SURGERY
Xuquan Ji, Yonghong Zhang, Yuanyuan Zhu, Biao Yang, Lei Hu, Yu Zhao, Wenyong Liu
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引用次数: 0

摘要

机器人辅助单侧双门静脉内窥镜手术(UBE)是一种比传统开放手术更准确、更安全的技术。超声钻孔的穿透识别仍然是机器人辅助UBE手术的挑战性技术之一。方法提出一种基于力和ae - mlp的实时穿透识别方法。在超声钻孔过程中,首先采集力信号并进行卡尔曼滤波去噪。然后利用预处理后的数据提取隐藏特征,分别通过变分自编码器(VAE)和多层感知器(MLP)进行分类,最终实现实时渗透识别。结果与经典时间序列分类算法相比,该方法的准确率(99.32% vs. 95.90%)更高,推理速度(17 ms vs. 33 ms)更快。机器人离体骨实验进一步验证了其有效性。结论力和VAE-MLP框架能够快速、准确地进行穿透检测,为减少UBE手术中神经损伤提供了可靠、高效的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Breakthrough Recognition in Robotic-Assisted UBE Surgery Based on Force Sensing and VAE-MLP

Background

Robotic-assisted unilateral biportal endoscopic surgery (UBE) is a more accurate and safer technique than traditional open surgical operations. The penetration recognition of ultrasonic drilling remains one of the challenging techniques of robotic-assisted UBE surgery.

Methods

We propose a force and VAE-MLP-based method for real-time penetration recognition. During the ultrasonic drilling procedure, the force signals are collected and denoised via Kalman filtering first. The pre-processed data are then used to extract hidden features and perform classification by Variational Autoencoder (VAE) and Multilayer Perceptron (MLP), respectively, ultimately achieving real-time penetration recognition.

Results

Our method achieves superior accuracy (99.32% vs. 95.90%) and faster inference speed (17 vs. 33 ms) compared to the classic time-series classification algorithm. Robotic ex vivo bone experiments further validated its efficacy.

Conclusion

The force and VAE-MLP framework enables fast and accurate penetration detection, which offers a reliable and efficient solution for minimizing nerve damage in UBE surgery.

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来源期刊
CiteScore
4.50
自引率
12.00%
发文量
131
审稿时长
6-12 weeks
期刊介绍: The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.
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