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SGR-AutoLap: Surgical gesture recognition-based autonomous laparoscope control for human-robot shared control in semi-autonomous minimally invasive surgical robot

Yanwen Sun (China North Industries North Automatic Control Technology Research Institute, Taiyuan, China)
Xiaojing Shi (China North Industries North Automatic Control Technology Research Institute, Taiyuan, China)
Shixun Zhai (China North Industries North Automatic Control Technology Research Institute, Taiyuan, China)
Kaige Zhang (China North Industries North Automatic Control Technology Research Institute, Taiyuan, China)
Bo Pan (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China)
Yili Fu (State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China)

Robotic Intelligence and Automation

ISSN: 2754-6969

Article publication date: 28 November 2024

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Abstract

Purpose

This paper aims to investigate the problem of vision based autonomous laparoscope control, which can serve as the primary function for semi-autonomous minimally invasive surgical robot system. Providing the surgical gesture recognition information is a fundamental key component for enabling intelligent context-aware assistance in autonomous laparoscope control task. While significant advances have been made in recent years, how to effectively carry out the efficient integration of surgical gesture recognition and autonomous laparoscope control algorithms for robotic assisted minimally invasive surgical robot system is still an open and challenging topic.

Design/methodology/approach

The authors demonstrate a novel surgeon in-loop semi-autonomous robotic-assisted minimally invasive surgery framework by integrating the surgical gesture recognition and autonomous laparoscope control tasks. Specifically, they explore using a transformer-based deep convolutional neural network to effectively recognize the current surgical gesture. Next, they propose an autonomous laparoscope control model to provide optimal field of view which is in line with surgeon intra-operation preferences.

Findings

The effectiveness of this surgical gesture recognition methodology is demonstrated on the public JIGSAWS and Cholec80 data sets, outperforming the comparable state-of-the-art methods. Furthermore, the authors have validated the effectiveness of the proposed semi-autonomous framework on the developed HUAQUE surgical robot platforms.

Originality/value

This study demonstrates the feasibility to perform cognitive assistant human–robot shared control for semi-autonomous robotic-assisted minimally invasive surgery, contributing to the reference for further surgical intelligence in computer-assisted intervention systems.

Keywords

Acknowledgements

Conflict of Interest: All authors declare that he has no conflict of interest.

Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

Citation

Sun, Y., Shi, X., Zhai, S., Zhang, K., Pan, B. and Fu, Y. (2024), "SGR-AutoLap: Surgical gesture recognition-based autonomous laparoscope control for human-robot shared control in semi-autonomous minimally invasive surgical robot", Robotic Intelligence and Automation, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/RIA-04-2024-0088

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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