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Design and navigation method of a soft robot for single-port transvesical radical prostatectomy

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

Abstract

Purpose

Currently, the rigid instruments used for laparoscopic radical resection of prostate cancer not only have the risk of damage to tissues, blood vessels, and nerves, but their limited freedom will also cause surgical blind areas. Soft robots are expected to solve these issues due to inherent flexibility, compliance, and safe interaction with tissues and organs. In addition, to achieve high surgical accuracy and provide precise guidance for surgeons, the navigation method should be studied for the soft robot.

Methods

A soft robot system for single-port transvesical radical prostatectomy (STRP) is developed, and a navigation method combining fiber Bragg gratings and electromagnetic tracking is proposed for the soft robot. To validate the soft robot design and the effectiveness of the navigation method, different groups of experiments are conducted.

Results

The proposed navigation method can achieve accurate location and shape sensing of the soft manipulator. The experiments show that the maximum tip sensing error is 2.691 mm, which is 5.38\(\%\) of the robot length for static configurations, and that the average tip sensing error is 1.966 mm, which corresponds to 3.93\(\%\) of the robot length for dynamic scenarios. Additionally, phantom tests demonstrate that the designed soft robot can enter the prostate through navigation guidance in a master–slave control mode and cover the entire prostate space.

Conclusions

The designed soft robot system, due to its soft structure, good flexibility, and accurate navigation, is expected to improve surgical safety and precision, thereby exhibiting significant potential for STRP.

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References

  1. Moradi S, Kelarijani MK, Shokri V (2021) Prostate cancer as a multifactorial disorder; an overview of different sides of disease. Central Asian J Med Pharm Sci Innov 3:143–150. https://doi.org/10.22034/CAJMPSI.2021.03.04

    Article  Google Scholar 

  2. Kaler J, Hussain A, Haque A, Naveed H, Patel S (2020) A comprehensive review of pharmaceutical and surgical interventions of prostate cancer. Cureus. https://doi.org/10.7759/cureus.11617

    Article  PubMed  PubMed Central  Google Scholar 

  3. Carbonara U, Srinath M, Crocerossa F, Ferro M, Cantiello F, Lucarelli G, Porpiglia F, Battaglia M, Ditonno P, Autorino R (2021) Robot-assisted radical prostatectomy versus standard laparoscopic radical prostatectomy: an evidence-based analysis of comparative outcomes. World J Urol. https://doi.org/10.1007/s00345-021-03687-5

    Article  PubMed  Google Scholar 

  4. Gao X, Pang J, Si-tu J, Luo Y, Zhang H, Li L-Y, Zhang Y (2013) Single-port transvesical laparoscopic radical prostatectomy for organ-confined prostate cancer: technique and outcomes. BJU Int 112(7):944–952. https://doi.org/10.1111/bju.12225

    Article  CAS  PubMed  Google Scholar 

  5. Zhou X, Fu B, Zhang C, Liu W, Guo J, Chen L, Lei E, Zhang X, Wang G (2020) Transvesical robot-assisted radical prostatectomy: initial experience and surgical outcomes. BJU Int 126(2):300–308. https://doi.org/10.1111/bju.15111

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhu J, Lyu L, Xu Y, Liang H, Zhang X, Ding H, Wu Z (2021) Intelligent soft surgical robots for next-generation minimally invasive surgery. Adv Intell Syst 3(5):2100011. https://doi.org/10.1002/aisy.202100011

    Article  Google Scholar 

  7. Zhang Y, Lu M (2020) A review of recent advancements in soft and flexible robots for medical applications. Int J Med Robot Comput Assist Surg 16(3):2096. https://doi.org/10.1002/rcs.2096

    Article  Google Scholar 

  8. Runciman M, Darzi A, Mylonas GP (2019) Soft robotics in minimally invasive surgery. Soft Robot 6(4):423–443. https://doi.org/10.1089/soro.2018.0136

    Article  PubMed  PubMed Central  Google Scholar 

  9. Gifari MW, Naghibi H, Stramigioli S, Abayazid M (2019) A review on recent advances in soft surgical robots for endoscopic applications. Int J Med Robot Comput Assist Surg 15(5):2010. https://doi.org/10.1002/rcs.2010

    Article  Google Scholar 

  10. McCandless M, Perry A, DiFilippo N, Carroll A, Billatos E, Russo S (2022) A soft robot for peripheral lung cancer diagnosis and therapy. Soft Robot 9(4):754–766. https://doi.org/10.1089/soro.2020.0127

    Article  PubMed  Google Scholar 

  11. Dai S, Hu C, Ma L, Zhang X, Zhang H, Liao H (2023) A stiffness-tunable soft actuator inspired by helix for medical applications. Int J Comput Assist Radiol Surg. https://doi.org/10.1007/s11548-023-02902-5

    Article  PubMed  Google Scholar 

  12. Fang G, Chow MC, Ho JD, He Z, Wang K, Ng T, Tsoi JK, Chan P-L, Chang H-C, Chan DT-M et al (2021) Soft robotic manipulator for intraoperative MRI-guided transoral laser microsurgery. Sci Robot 6(57):5575. https://doi.org/10.1126/scirobotics.abg5575

    Article  Google Scholar 

  13. Li Y, Liu Y, Yamazaki K, Bai M, Chen Y (2021) Development of a soft robot based photodynamic therapy for pancreatic cancer. IEEE/ASME Trans Mechatron 26(6):2977–2985. https://doi.org/10.1109/TMECH.2021.3049354

    Article  Google Scholar 

  14. Li R, Chen F, Yu W, Igarash T, Shu X, Xie L (2022) A novel cable-driven soft robot for surgery. J Shanghai Jiaotong University (Sci). https://doi.org/10.1007/s12204-022-2497-3

    Article  PubMed Central  Google Scholar 

  15. Carriere J, Rossa C, Sloboda R, Usmani N, Tavakoli M (2016) Real-time needle shape prediction in soft-tissue based on image segmentation and particle filtering. In: 2016 IEEE international conference on advanced intelligent mechatronics (AIM), pp 1204–1209. https://doi.org/10.1109/AIM.2016.7576934

  16. Hoffmann M, Brost A, Koch M, Bourier F, Maier A, Kurzidim K, Strobel N, Hornegger J (2015) Electrophysiology catheter detection and reconstruction from two views in fluoroscopic images. IEEE Trans Med Imaging 35(2):567–579. https://doi.org/10.1109/TMI.2015.2482539

    Article  PubMed  Google Scholar 

  17. Franz AM, Haidegger T, Birkfellner W, Cleary K, Peters TM, Maier-Hein L (2014) Electromagnetic tracking in medicinea review of technology, validation, and applications. IEEE Trans Med Imaging 33(8):1702–1725. https://doi.org/10.1109/TMI.2014.2321777

    Article  PubMed  Google Scholar 

  18. Song S, Li Z, Meng MQ-H, Yu H, Ren H (2015) Real-time shape estimation for wire-driven flexible robots with multiple bending sections based on quadratic bézier curves. IEEE Sens J 15(11):6326–6334. https://doi.org/10.1109/JSEN.2015.2456181

    Article  Google Scholar 

  19. Song S, Li Z, Yu H, Ren H (2015) Electromagnetic positioning for tip tracking and shape sensing of flexible robots. IEEE Sens J 15(8):4565–4575. https://doi.org/10.1109/JSEN.2015.2424228

    Article  Google Scholar 

  20. Abbas A, Zhao J (2017) Twisted and coiled sensor for shape estimation of soft robots. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 482–487. https://doi.org/10.1109/IROS.2017.8202197

  21. So J, Kim U, Kim YB, Seok D-Y, Yang SY, Kim K, Park JH, Hwang ST, Gong YJ, Choi HR (2021) Shape estimation of soft manipulator using stretchable sensor. Cyborg Bionic Syst. https://doi.org/10.34133/2021/9843894

    Article  PubMed  PubMed Central  Google Scholar 

  22. Osman D, Du X, Li W, Noh Y (2022) Development of an optical shape sensing method using optoelectronic sensors for soft flexible robotic manipulators in mis. IEEE Trans Med Robot Bionics 4(2):343–347. https://doi.org/10.1109/TMRB.2022.3155200

    Article  Google Scholar 

  23. McCandless M, Wise FJ, Russo S (2023) A soft continuum robot with multi-modal shape and contact force sensing for minimally invasive surgery. In: Proceedings of The 15th Hamlyn symposium on medical robotics 2023, pp 87–88. https://doi.org/10.31256/hsmr2023.46

  24. Khan F, Denasi A, Barrera D, Madrigal J, Sales S, Misra S (2019) Multi-core optical fibers with BRAGG gratings as shape sensor for flexible medical instruments. IEEE Sens J 19(14):5878–5884. https://doi.org/10.1109/JSEN.2019.2905010

    Article  Google Scholar 

  25. Cao Y, Liu Z, Yu H, Hong W, Xie L (2023) Spatial shape sensing of a multisection continuum robot with integrated DTG sensor for maxillary sinus surgery. IEEE/ASME Trans Mechatron 28(2):715–725. https://doi.org/10.1109/TMECH.2022.3205604

    Article  Google Scholar 

  26. Khan F, Donder A, Galvan S, Baena FR, Misra S (2020) Pose measurement of flexible medical instruments using fiber BRAGG gratings in multi-core fiber. IEEE Sens J 20(18):10955–10962. https://doi.org/10.1109/JSEN.2020.2993452

    Article  CAS  Google Scholar 

  27. Liu Z, Li R, Cao Y, Feng F, Xie L (2022) Shape sensing of a soft robot using fiber bragg gratings for single-port transvesical radical prostatectomy. In: 2022 IEEE international conference on robotics and biomimetics (ROBIO), pp 2087–2092. https://doi.org/10.1109/ROBIO55434.2022.10012004

  28. Yiasemidou M, Glassman D, Biyani, CS (2017) Instruments in laparoscopic reconstructive urology. Operative Atlas of Laparoscopic and Robotic Reconstructive Urology: Second Edition, 39–47. https://doi.org/10.1007/978-3-319-33231-4_4

  29. Koukourikis P, Rha KH (2021) Robotic surgical systems in urology: what is currently available? Investig Clin Urol 62(1):14. https://doi.org/10.4111/icu.20200387

    Article  PubMed  Google Scholar 

  30. Reeves F, Everaerts W, Murphy DG, Costello A (2016) The surgical anatomy of the prostate. In: Prostate cancer, pp 253–263. https://doi.org/10.1016/B978-0-12-800077-9.00029-3

  31. Alciatore DG, Ng C-CD (1994) Determining manipulator workspace boundaries using the monte carlo method and least squares segmentation. In: International design engineering technical conferences and computers and information in engineering conference, vol 12860, pp 141–146. https://doi.org/10.1115/DETC1994-0316

  32. Othonos A (1997) Fiber bragg gratings. Rev Sci Instr 68(12):4309–4341. https://doi.org/10.1063/1.1148392

  33. Arun KS, Huang TS, Blostein SD (1987) Least-squares fitting of two 3-d point sets. IEEE Trans Pattern Anal Mach Intell PAMI 9(5):698–700. https://doi.org/10.1109/TPAMI.1987.4767965

    Article  CAS  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant 62133009, Grant 61973211, Grant 62211540723 and Grant M-0221), in part by the Science and Technology Commission of Shanghai Municipality (Grant 21550714200 and Grant 20DZ2220400), in part by the project of Institute of Medical Robotics of Shanghai Jiao Tong University, in part by the Foreign Cooperation Project of Fujian Province Science and Technology Program under Grant (No. 2022I0041), in part by the project of Quanzhou High-level Talent Innovation and Entrepreneurship (No. 2021C003R), in part by the Hospital-local Cooperation Project of Xuhui District Artificial Intelligence Medicine (No. 2021-008), and in part by the Joint Project of Xinhua Hospital and Institute of Medical Robotics of Shanghai Jiao Tong University(No. 21XJMR03).

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Correspondence to Le Xie.

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Liu, Z., Li, R., Cao, Y. et al. Design and navigation method of a soft robot for single-port transvesical radical prostatectomy. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03122-1

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