Advertisement

A Noninvasive Calibration-Free and Model-Free Surgical Robot for Automatic Fracture Reduction

  • Shijie Zhu
  • Yitong Chen
  • Yu Chen
  • Jiawei Sun
  • Zhe Zhao
  • Changping Hu
  • Gangtie ZhengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11745)

Abstract

Surgical robots for femoral fracture reduction have enjoyed a surge of interest among surgeons recently because robots can avoid problems like over radiation and insufficient accuracy. However, tedious calibration procedures, complicated tissue modeling and hurtful invasive fixation restrict their clinical application. Here we introduce a novel fracture reduction idea based on visual servo to eliminate calibration, kinematics and muscle modeling and invasive markers to finish femoral fracture reduction simply and automatically. It employs images from two perpendicular directions to estimate the mapping from robot movements to displacements of limbs. We also present its satisfactory performance on simulation and skeleton experiments. Our method shows rapid convergence, stable precision and adequate domain of convergence under various circumstances. Hopefully this technique will enable a surgeon to manage several surgeries simultaneously, which offers brand new possibilities for present medical treatment.

Keywords

Calibration-free Model-free Noninvasive Automatic fracture reduction 

Notes

Acknowledgement

We thank Boyuan Deng from TEEP, Tsinghua University and Dr. Yongwei Pan from Tsinghua Changgung Hospital for their help during design and experiments. This research is funded by Tsinghua University.

References

  1. 1.
    Füchtmeier, B., et al.: Reduction of femoral shaft fractures in vitro by a new developed reduction robot system ‘RepoRobo’. Inj.-Int. J. Care Inj. 35(1), 113–119 (2004)CrossRefGoogle Scholar
  2. 2.
    Mastrangelo, G., et al.: Increased cancer risk among surgeons in an orthopaedic hospital. Occup. Med. (Lond) 55(6), 498–500 (2005)CrossRefGoogle Scholar
  3. 3.
    Westphal, R., et al.: Automated robot assisted fracture reduction. In: Kröger, T., Wahl, F.M. (eds.) Advances in Robotics Research, pp. 251–262. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01213-6_23CrossRefGoogle Scholar
  4. 4.
    Joung, S., Park, I.: Medical robotics for musculoskeletal surgery. In: Zheng, G., Li, S. (eds.) Computational Radiology for Orthopaedic Interventions. LNCVB, vol. 23, pp. 299–332. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-23482-3_15CrossRefGoogle Scholar
  5. 5.
    Westphal, R., et al.: Robot-assisted long bone fracture reduction. Int. J. Robot. Res. 28(10), 1259–1278 (2009)CrossRefGoogle Scholar
  6. 6.
    Suero, E.M., et al.: Improving the human–robot interface for telemanipulated robotic long bone fracture reduction: joystick device vs. haptic manipulator. Int. J. Med. Robot. Comput. Assist. Surg. e1863-n/aGoogle Scholar
  7. 7.
    Shirai, Y., Inoue, H.: Guiding a robot by visual feedback in assembling tasks. Pattern Recognit. 5(2), 99–106 (1973)CrossRefGoogle Scholar
  8. 8.
    Hoeckelmann, M., Rudas, I.J., Fiorini, P., Kirchner, F., Haidegger, T.: Current capabilities and development potential in surgical robotics. Int. J. Adv. Robot. Syst. 12, 61 (2015)CrossRefGoogle Scholar
  9. 9.
    Du, H., et al.: Advancing computer-assisted orthopaedic surgery using a hexapod device for closed diaphyseal fracture reduction. Int. J. Med. Robot. Comput. Assist. Surg. Mrcas 11(3), 348–359 (2014)CrossRefGoogle Scholar
  10. 10.
    Qian, J., Su, J.: Online estimation of image Jacobian matrix by Kalman-Bucy filter for uncalibrated stereo vision feedback. In: IEEE International Conference on Robotics and Automation, Proceedings, ICRA, vol. 1. pp. 562–567 (2002)Google Scholar
  11. 11.
    Auger, F., et al.: Industrial applications of the Kalman filter: a review. IEEE Trans. Ind. Electron. 60(12), 5458–5471 (2013)CrossRefGoogle Scholar
  12. 12.
    Yang, C., et al.: Forward kinematics analysis of parallel manipulator using modified global Newton-Raphson method. In: International Conference on Intelligent Computation Technology & Automation (2009)Google Scholar
  13. 13.
    Suero, E.M., et al.: Improving the human-robot interface for telemanipulated robotic long bone fracture reduction: Joystick device vs. haptic manipulator. Int. J. Med. Robot. Comput. Assist. Surg. 14(1), e1863 (2018)CrossRefGoogle Scholar
  14. 14.
    Du, H., et al.: Preoperative trajectory planning for closed reduction of long-bone diaphyseal fracture using a computer-assisted reduction system. Int. J. Med. Robot. Comput. Assist. Surg. 11(1), 58–66 (2015)CrossRefGoogle Scholar
  15. 15.
    Kim, W.Y., Ko, S.Y.: Hands-on robot-assisted fracture reduction system guided by a linear guidance constraints controller using a pre-operatively planned goal pose. Int. J. Med. Robot. Comput. Assist. Surg. 15, e1967 (2018)CrossRefGoogle Scholar
  16. 16.
    Abedinnasab, M.H., Farahmand, F., Gallardo-Alvarado, J.: The wide-open three-legged parallel robot for long-bone fracture reduction. J. Mech. Robot.-Trans. Asme 9(1), 015001 (2017)CrossRefGoogle Scholar
  17. 17.
    Li, C.S., et al.: A novel master-slave teleoperation robot system for diaphyseal fracture reduction: a preliminary study. Comput. Assist. Surg. 21, 163–168 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shijie Zhu
    • 1
  • Yitong Chen
    • 1
  • Yu Chen
    • 1
  • Jiawei Sun
    • 1
  • Zhe Zhao
    • 2
  • Changping Hu
    • 1
  • Gangtie Zheng
    • 1
    Email author
  1. 1.School of Aerospace EngineeringTsinghua UniversityBeijingChina
  2. 2.Department of OrthopaedicsBeijing Tsinghua Changgung HospitalBeijingChina

Personalised recommendations