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Online advance respiration prediction model for percutaneous puncture robotics

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Surgical robots have significant research value and clinical significance in the field of percutaneous punctures. There have been numerous studies on ultrasound-guided percutaneous surgical robots; however, addressing the respiratory compensation problem of deep punctures remains a significant obstacle. Herein we propose a robotic system for percutaneous puncture with respiratory compensation.

Methods

We proposed an online advance respiratory prediction model based on Bidirectional Gate Recurrent Unit (Bi-GRU) for the respiratory prediction requirements of surgical robot systems. By analyzing the main factors governing the accuracy of the respiratory motion prediction models, various parameters of the online advance prediction model were optimized. Subsequently, we integrated and developed ultrasound-guided percutaneous puncture robot software and a hardware platform to implement respiratory compensation, thus verifying the effectiveness and reliability of various key technologies in the system.

Results

The proposed respiratory prediction model has a significantly reduced update time, with an average root mean square error (RMSE) of less than 0.4 mm. This represents a reduction of ~ 20% compared to the online training long short-term memory(LSTM). By conducting puncture experiments based on a respiratory phantom, the average puncture error was 2.71 ± 0.65 mm and the average single-round puncture time was 65.00 ± 6.67 s.

Conclusion

Herein we proposed and optimized an online training respiratory prediction network model based on Bi-GRU. The stability and reliability of this system are verified by conducting puncture experiments on a respiratory phantom.

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Acknowledgements

We would like to thank Editage (www.editage.cn) for the English language editing.

Funding

This study was funded by the National Natural Science Foundation of China (Grant number 52175020).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China

    Yanping Lin, Jin Guo, Xu Yang & Wangjie Xu

  2. Department of Ultrasound, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China

    Zhaojun Li

  3. Department of Ultrasound, Shanghai General Hospital Jiading Branch, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China

    Zhaojun Li

Authors
  1. Yanping Lin
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  2. Jin Guo
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  3. Xu Yang
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  4. Wangjie Xu
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Correspondence to Yanping Lin.

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The authors declare that they have no conflicts of interest.

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All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. No animal experiments were performed in this study.

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Lin, Y., Guo, J., Yang, X. et al. Online advance respiration prediction model for percutaneous puncture robotics. Int J CARS 19, 383–394 (2024). https://doi.org/10.1007/s11548-023-03041-7

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  • DOI: https://doi.org/10.1007/s11548-023-03041-7

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