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A New Breakthrough Detection Method for Bone Drilling in Robotic Orthopedic Surgery with Closed-Loop Control Approach

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Abstract

Breakthrough detection is a crucial task to reduce the risks of damaging soft tissue bone drilling operations during orthopedic surgery. Conventional drills are not equipped with this function while the recent literature has offered this capability with high cost and complex modification needs. In this study, a new breakthrough detection approach based on closed-loop control characteristics of the drilling operation is proposed. A feature set containing closed-loop signals and force sensor data is created to train K-Nearest and Ensemble Classifier for breakthrough detection tasks with drilling the synthetic bone model and animal bone with a robot manipulator. The best accuracy of breakthrough detection with only closed-loop control signal attributes is achieved as 96.9 ± 0.8% for the synthetic bone model and 98.1 ± 0.2% for sheep femur bone. Breakthrough detection delay which included sampling and operation time of the method guarantees that the drill bit would stop with acceptable breakthrough range of 1.0413 mm. The proposed method can be used to detect breakthrough and also to estimate the state of the drill bit in robotic orthopedic bone drilling processes using only closed-loop signals so that it would be no need to use extra high-cost sensors.

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References

  1. Åström, K. J., and T. Hägglund. PID controllers: theory, design, and tuning. North Carolina: Instrument Society of America Research Triangle Park, 1995.

    Google Scholar 

  2. Aziz, M. H., M. A. Ayub, and R. Jaafar. Real-time algorithm for detection of breakthrough bone drilling. Procedia Eng. 41:352–359, 2012.

    Article  Google Scholar 

  3. Boiadjiev, G., I. Chavdarov, K. Delchev, T. Boiadjiev, R. Kastelov, and K. Zagurki. Development of hand-held surgical robot ODRO-2 for automatic bone drilling. J. Theor. Appl. Mech. 47:12–22, 2017.

    Article  Google Scholar 

  4. Brett, P. N., D. A. Baker, and F. Naghdy. Automatic detection of normal drill breakthrough through planar bone tissues of unknown thickness. IFAC Proc. 30:609–612, 1997.

    Article  Google Scholar 

  5. Clement, H., N. Heidari, W. Grechenig, A. M. Weinberg, and W. Pichler. Drilling, not a benign procedure: laboratory simulation of true drilling depth. Injury 43:950–952, 2012.

    Article  Google Scholar 

  6. Dai, Y., Y. Xue, and J. Zhang. Drilling electrode for real-time measurement of electrical impedance in bone tissues. Ann. Biomed. Eng. 42:579–588, 2014.

    Article  Google Scholar 

  7. Deng, Z., H. Jin, Y. Hu, Y. He, P. Zhang, W. Tian, and J. Zhang. Fuzzy force control and state detection in vertebral lamina milling. Mechatronics 35:1–10, 2016.

    Article  CAS  Google Scholar 

  8. Farouk, O., C. Krettek, T. Miclau, P. Schandelmaier, P. Guy, and H. Tscherne. Minimally invasive plate osteosynthesis: does percutaneous plating disrupt femoral blood supply less than the traditional technique? J. Orthop. Trauma 13:401–406, 1999.

    Article  CAS  Google Scholar 

  9. Gönen, E. Minimally invasive surgical techniques for the treatment of the shaft fractures of the long bones. Türk Ortop. ve Travmatoloji Birliği Derneği Derg. 11:78–88, 2012.

    Article  Google Scholar 

  10. Hansen, P. C., V. Pereyra, and G. Scherer. Least squares data fitting with applications. Baltimore: JHU Press, 2012.

    Google Scholar 

  11. Hessinger, M., J. Hielscher, P. P. Pott, and R. Werthschutzky. Handheld surgical drill with integrated thrust force recognition. 2013. https://doi.org/10.1109/EHB.2013.6707303.

    Article  Google Scholar 

  12. Jaskowiak, P. A., and R. J. G. B. Campello. Comparing Correlation Coefficients as Dissimilarity Measures for Cancer Classification in Gene Expression Data. Proceedings of the Brazilian Symposium on Bioinformatics, pp. 1–8. Brasília, 2011.

  13. Jin, H., Y. Hu, Z. Deng, P. Zhang, Z. Song, and J. Zhang. Model-based state recognition of bone drilling with robotic orthopedic surgery system. Conf. Robot. Autom. 3538–3543:2014, 2014.

    Google Scholar 

  14. Ko, P. J., and M. C. Tsai. A novel measurement for monitoring mechanical impedance applied to breakthrough detection of bone-drill system. IOP Conf. Ser. Mater. Sci. Eng. 398:012028, 2018.

    Article  Google Scholar 

  15. Kuo, B. C., and F. Golnaraghi. Automatic control systems. NJ: Prentice-Hall Englewood Cliffs, 1995.

    Google Scholar 

  16. Lee, W. Y., and C. L. Shih. Force control and breakthrough detection of a bone drilling system. 2003 IEEE Int. Conf. Robot. Autom. (Cat. No. 03CH37422). 2:1787–1792, 2003.

  17. Lee, W.-Y., and C.-L. Shih. Control and breakthrough detection of a three-axis robotic bone drilling system. Mechatronics 16:73–84, 2006.

    Article  CAS  Google Scholar 

  18. Li, Y., X. Li, G. U. Feng, Z. Gao, and P. Shen. New method for identifying abnormal milling states of an otological drill. Med. Devices Evid. Res. 8:207–218, 2015.

    Article  Google Scholar 

  19. Mucherino, A., P. J. Papajorgji, and P. M. Pardalos. Data mining in agriculture. Clim. Chang. 2013—Phys. Sci. Basis 34:1–30, 2009.

  20. Novitskaya, E., P. Y. Chen, S. Lee, A. Castro-Ceseña, G. Hirata, V. A. Lubarda, and J. McKittrick. Anisotropy in the compressive mechanical properties of bovine cortical bone and the mineral and protein constituents. Acta Biomater. 7:3170–3177, 2011.

    Article  CAS  Google Scholar 

  21. Osa, T., C. F. Abawi, N. Sugita, H. Chikuda, S. Sugita, H. Ito, T. Moro, Y. Takatori, S. Tanaka, and M. Mitsuishi. Autonomous penetration detection for bone cutting tool using demonstration-based learning. Proc.—IEEE Int. Conf. Robot. Autom. 290–296, 2014. https://doi.org/10.1109/ICRA.2014.6906624

  22. Osa, T., C. F. Abawi, N. Sugita, H. Chikuda, S. Sugita, T. Tanaka, H. Oshima, T. Moro, S. Tanaka, and M. Mitsuishi. Hand-held bone cutting tool with autonomous penetration detection for spinal surgery. IEEE/ASME Trans. Mechatronics 20:3018–3027, 2015.

    Article  Google Scholar 

  23. Pandey, R. K., and S. S. Panda. Bone drilling: an area seeking for improvement. 2011 Nirma Univ. Int. Conf. Eng. Curr. Trends Technol. NUiCONE 2011—Conf. Proc. 8–10, 2011. https://doi.org/10.1109/NUiConE.2011.6153315

  24. Polikar, R. Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6:21–44, 2006.

    Article  Google Scholar 

  25. Qi, L., and M. Q. H. Meng. Real-time break-through detection of bone drilling based on wavelet transform for robot assisted orthopaedic surgery. 2014 IEEE Int. Conf. Robot. Biomimetics, IEEE ROBIO 2014 601–606, 2014. https://doi.org/10.1109/ROBIO.2014.7090396

  26. Rahman, K. M., and S. Hiti. Identification of machine parameters of a synchronous motor. IEEE Trans. Ind. Appl. 41:557–565, 2005.

    Article  Google Scholar 

  27. Shen, P., F. Guo-dong, C. Tian-yang, G. Zhi-qiang, and L. Xi-sheng. An intelligent otologic drill. J. Otol. 5:104–110, 2010.

    Article  Google Scholar 

  28. Sui, J., and N. Sugita. Experimental study of thrust force and torque for drilling cortical bone. Ann. Biomed. Eng. 47:802–812, 2019.

    Article  Google Scholar 

  29. Sui, J., and N. Sugita. Experimental study of thrust force and torque for drilling cortical bone. Ann. Biomed. Eng. 47(3):802–812, 2019.

    Article  Google Scholar 

  30. Taha, Z., A. M. Salah, and J. V Lee. Bone breakthrough detection for orthopedic robot—assisted surgery. APIEMS 2008 Proc. 9th Asia Pasific Ind. Eng. Manag. Syst. Conf. 2742–2746, 2008

  31. Thabtah, F., S. Hammoud, F. Kamalov, and A. Gonsalves. Data imbalance in classification: experimental evaluation. Inf. Sci. (Ny) 2019. https://doi.org/10.1016/J.INS.2019.11.004.

    Article  Google Scholar 

  32. Torun, Y., A. Ozturk, A. Aksoz, and O. Pazarci. Parameters estimation of orthopedic drill 2019. https://doi.org/10.1109/siu.2019.8806501.

    Article  Google Scholar 

  33. Torun, Y., A. Ozturk, N. Hatipoglu, and Z. Oztemur. Breakthrough detection for orthopedic bone drilling via power spectral density estimation of acoustic emission. 2018 Electr. Electron. Comput. Sci. Biomed. Eng. Meet. EBBT 2018 1–5, 2018. https://doi.org/10.1109/EBBT.2018.8391464

  34. Wang, Y., Z. Deng, Y. Sun, B. Yu, P. Zhang, Y. Hu, and J. Zhang. State detection of bone milling with multi-sensor information fusion. Int. Conf. Robot. Biomimetics 1:1643–1648, 2015. https://doi.org/10.1109/robio.2015.7419007.

    Article  Google Scholar 

  35. Yokota, T., M. Gen, and Y. X. Li. Genetic algorithm for non-linear mixed integer programming problems and its applications. Comput. Ind. Eng. 30:905–917, 1996.

    Article  Google Scholar 

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Acknowledgments

This study was financially supported by Cumhuriyet University Scientific Research Grant Program with M-736 project code. The authors would like to thank Dr. Ozhan PAZARCI for his valuable comments during the experimental study.

Conflict of interest

The authors declare that there is no conflict of interest.

Funding

This work is supported by Cumhuriyet University Scientific Research Grant Programme with M-736 project code

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Authors and Affiliations

  1. Electrical and Electronic Engineering Department ROBOLAB, Sivas Cumhuriyet University, 58140, Sivas, Turkey

    Yunis Torun & Ahmet Öztürk

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  1. Yunis Torun
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  2. Ahmet Öztürk
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Correspondence to Yunis Torun.

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Associate Editor Umberto Morbiducci oversaw the review of this article.

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Torun, Y., Öztürk, A. A New Breakthrough Detection Method for Bone Drilling in Robotic Orthopedic Surgery with Closed-Loop Control Approach. Ann Biomed Eng 48, 1218–1229 (2020). https://doi.org/10.1007/s10439-019-02444-5

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  • DOI: https://doi.org/10.1007/s10439-019-02444-5

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