DP4AuSu: Autonomous Surgical Framework for Suturing Manipulation Using Diffusion Policy With Dynamic Time Wrapping-Based Locally Weighted Regression

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2025-05-19 DOI:10.1002/rcs.70072

Wenda Xu, Zhihang Tan, Zexin Cao, Haofei Ma, Gongcheng Wang, Han Wang, Weidong Wang, Zhijiang Du

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引用次数: 0

Abstract

Background

Emerging imitation learning (IL) approaches have provided innovative solutions for completing surgical robotic suturing autonomously, significantly aiding surgeons in their manipulations.

Methods

We introduce Diffusion Policy for Autonomous Suturing (DP4AuSu), a novel framework that leverages diffusion policy (DP) and dynamic time wrapping-based locally weighted regression to achieve autonomous robotic suturing.

Results

In simulation, DP4AuSu achieved a 94% success rate for insertion subtasks over 50 trials. In a real-world setting, it achieves 85% success rate over 20 trials for suturing manipulations in 390.55–41.59s faster than conventional diffusion policy.

Conclusions

Our novel framework can capture the multimodality in demonstrations and successfully learn the suturing policy and reduce the suturing time. To the best of our knowledge, this work represents the first application of diffusion policy for robotic suturing. We hope this research paves the way for the automation of more complex surgical tasks.

查看原文本刊更多论文

DP4AuSu：基于动态时间包裹的局部加权回归扩散策略的自主缝合手术框架

新兴的模仿学习（IL）方法为自主完成手术机器人缝合提供了创新的解决方案，极大地帮助了外科医生的操作。方法引入自主缝合的扩散策略（Diffusion Policy for Autonomous suture, DP4AuSu），这是一个利用扩散策略（Diffusion Policy， DP）和基于动态时间包裹的局部加权回归来实现机器人自主缝合的新框架。结果在仿真中，DP4AuSu在50次试验中插入子任务的成功率达到94%。在现实环境中，与传统的扩散策略相比，它在390.55 - 41.59秒内完成了20次缝合操作，成功率达到85%。结论该框架能有效地捕捉多模态，有效地学习缝合策略，缩短缝合时间。据我们所知，这项工作代表了扩散策略在机器人缝合中的首次应用。我们希望这项研究能为更复杂的手术任务的自动化铺平道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

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.