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Theory and Application of High-Precision Preoperative Positioning for Cochlear Surgical Robot

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Abstract

According to a World Health Organization survey report in 2021, 430 million people worldwide currently suffer from moderate or severe hearing impairment. Cochlear implant surgery is one of the most effective ways to restore hearing impairment, however, it can be very difficult for doctors. Therefore, surgery with the help of surgical robots has become a popular research topic. To meet the needs of surgery, there are strict requirements for the precision of surgical robots, such as the cochlear implant robot we designed in this paper. So we mainly design a complete precision evaluation theory for this cochlear implant robot in this paper. By analyzing the jacobian matrix of the manipulator, the surgical position and posture of the robot with optimal precision can be calculated based on this theory. This new computational theory enables the robot to meet surgical precision requirements within a limited area, and to make this theory more practical, we developed a vision-based system to assist doctors during preoperative positioning and ensure that the surgical area is in the robot’s optimal precision area.

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Code or data availability

We used the public data set: Biwi Kinect Head Pose Database [47].

References

  1. WHO: The world report on hearing: executive summary 3 1–12 (2021)

  2. Stevens, S.S.: On hearing by electrical stimulation. Journal of the Acoustical Society of America 8(3), 191–195 (1937)

    Article  Google Scholar 

  3. Eshraghi, A.A., Nazarian, R., Telischi, F.F., Rajguru, S.M., Truy, E., Gupta, C.: The cochlear implant: Historical aspects and future prospects, The anatomical record: advances in integrative anatomy and evolutionary biology (1980)

  4. Simmons, F.B., Epley, J.M., Lummis, R.C., Guttman, N., Frishkopf, L.S., Harmon, L.D., Zwicker, E.: Auditory nerve: Electrical stimulation in man. Science 148(3666), 104–106 (1965)

    Article  Google Scholar 

  5. M. A. Som Da S, P. Li, D. M. Whiten, D. K. Eddington, J. B. Nadol, Quantitative evaluation of new bone and fibrous tissue in the cochlea following cochlear implantation in the human., Audiology and Neuro-Otology 12(5): 277–284 (2006)

  6. Seyyedi, M., Nadol, Jr.B.: Intracochlear inflammatory response to cochlear implant electrodes in humans., Otology snd neurotology : official publication of the American Otological Society, American Neurotology Society European Academy of Otology and Neurotology (2014)

  7. Parikh, P., Trivedi, R., Dave, J., Joshi, K., Adhyaru, D.: Design and development of a low-cost vision-based 6 dof assistive feeding robot for the aged and specially-abled people. IETE J Res. (2023)

  8. Swarup, A., Gopal, M.: Control strategies for robot manipulators-a review. IETE J. Res. (1989)

  9. Singh, S., Khosla, A., Kapoor, R.: Visual-thermal fusion-based object tracking via a granular computing backed particle filtering. IETE J. Res. (2022)

  10. Asha, C.S., Narasimhadhan, A.V.: Visual tracking using kernelized correlation filter with conditional switching to median flow tracker. IETE J. Res. (2020)

  11. Kumar, A.: Real-time performance comparison of vision-based autonomous landing of quadcopter on a ground moving target. IETE J. Res. (2021)

  12. Rao, D.H., Kamat, H.V.: Neuro-fuzzy system for robotics applications. IETE J. Res. 42(4/5), 325–333 (1996)

    Article  Google Scholar 

  13. El Hamidi, K., Mjahed, M., El Kari, A., Ayad, H., El Gmili, N.: Design of hybrid neural controller for nonlinear mimo system based on narma-l2 model. IETE J. Res. (2021)

  14. Jiang, D., Li, G., Sun, Y., Hu, J., Yun, J., Liu, Y.: Manipulator grabbing position detection with information fusion of color image and depth image using deep learning. J Ambient Intell Humaniz, Comput (2021)

    Book  Google Scholar 

  15. Li, X.: Robot target localization and interactive multi-mode motion trajectory tracking based on adaptive iterative learning. J. Ambient Intell. Humaniz, Comput (2020)

    Book  Google Scholar 

  16. Tong, C.: Three-dimensional reconstruction of the dribble track of soccer robot based on heterogeneous binocular vision. J. Ambient Intell. Humaniz, Comput (2020)

    Book  Google Scholar 

  17. An, X., Wang, Y.: Smart wearable medical devices for isometric contraction of muscles and joint tracking with gyro sensors for elderly people. J. Ambient Intell Humaniz, Comput (2021)

    Book  Google Scholar 

  18. Ding, H.: Motion path planning of soccer training auxiliary robot based on genetic algorithm in fixed-point rotation environment. J. Ambient Intell Humaniz, Comput (2020)

    Book  Google Scholar 

  19. Li, J.: Zhou, N., Wang, S., Gao, Y., Liu, D.: Design of an integrated master-slave robotic system for minimally invasive surgery. Int. J. Med. Robot. Comput. Assist. Surg. (2012)

  20. Harris, S.J., Arambula-Cosio, F., Mei, Q., R, D, H., The probot - an active robot for prostate resection. Proc. Inst. Mech. Eng. H J. Eng. Med. (1997)

  21. Gyung, Tak, Sung: Inderbir, S, Gill, Robotic laparoscopic surgery: a comparison of the da vinci and zeus systems. Urology (2001)

  22. Guthart G.S., Salisbury, J.K.: The intuitivetm telesurgery system: overview and application, in: IEEE International Conference on Robotics and Automation. pp. 618–621 (2000)

  23. Hayashibe, M., Suzuki, N., Hashizume, M., Konishi, K., Hattori, A.: Robotic surgery setup simulation with the integration of inverse-kinematics computation and medical imaging. Comput. Methods Prog. Biomed. 83(1), 63–72 (2006)

    Article  Google Scholar 

  24. O’Malley, B.W., Weinstein, G.S., Snyder, W., Hockstein, N.G.: Transoral robotic surgery (tors) for base of tongue neoplasms. Laryngoscope 116(8), 1465–1472 (2010)

    Article  Google Scholar 

  25. Liu, W.P., Azizian, M., Sorger, J., Taylor, R.H., Reilly, B.K., Cleary, K., Preciado, D.: Cadaveric feasibility study of da vinci si-assisted cochlear implant with augmented visual navigation for otologic surgery. JAMA otolaryngology- head and neck surgery 140(3), 208 (2014)

    Article  Google Scholar 

  26. Bell, B., Stieger, C., Gerber, N., Arnold, A., Nauer, C., Hamacher, V., Kompis, M., Nolte, L., Caversaccio, M., Weber, S.: A self-developed and constructed robot for minimally invasive cochlear implantation. Acta Otolaryngol 132(4), 355–360 (2012)

    Article  Google Scholar 

  27. Gerber, N., Bell, B., Gavaghan, K., Weisstanner, C., Caversaccio, M. and, Surgical planning tool for robotically assisted hearing aid implantation, International Journal of Computer Assisted Radiology and Surgery (2014)

  28. Bell, B., Gerber, N., Williamson, T., Gavaghan, K., Weber, S.: In vitro accuracy evaluation of image-guided robot system for direct cochlear access, Otology and neurotology: official publication of the American Otological Society, American Neurotology Society and European Academy of. Otology and Neurotology 34(7), 1284–90 (2013)

    Article  Google Scholar 

  29. Feldmann, A., Anso, J., Bell, B., Williamson, T., Gavaghan, K., Gerber, N., Rohrbach, H., Weber, S., Zysset, P.: Temperature prediction model for bone drilling based on density distribution and in vivo experiments for minimally invasive robotic cochlear implantation. Ann. Biomed, Eng (2016)

    Book  Google Scholar 

  30. M. Caversaccio, K. Gavaghan, W. Wimmer, T. Williamson, J. Anso, G. Mantokoudis, N. Gerber, C. Rathgeb, A. Feldmann, F. a. Wagner, Robotic cochlear implantation: surgical procedure and first clinical experience. Acta Oto Laryngologica 137(4):447–454(2017)

  31. Miroir, M., Szewczyk, J., Nguyen, Y., Mazalaigue, S., Sterkers, O.: Design of a robotic system for minimally invasive surgery of the middle ear, in: IEEE Ras and Embs International Conference on Biomedical Robotics and Biomechatronics (2015)

  32. Baron, S., Eilers, H., Munske, B., Toennies, J.L., Webster, R.J.: Percutaneous inner-ear access via an image-guided industrial robot system. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine 224(5), 633–649 (2010)

  33. Wanna, G.B., Balachandran, R., Majdani, O., Mitchell, J., Labadie, R.F.: Percutaneous access to the petrous apex in vitro using customized micro-stereotactic frames based on image-guided surgical technology. Acta oto-laryngologica 130(4), 1–6 (2010)

    Article  Google Scholar 

  34. Schipper, J., Aschendorff, A., Arapakis, I., Klenzner, T., Teszler, C. B., Ridder, G.J., Laszig, R.: Navigation as a quality management tool in cochlear implant surgery. J. Laryngol. Otol. 118(10) (2004)

  35. Rau, T.S., Kreul, D., Lexow, J., Hügl, S., Zuniga, M.G., Lenarz, T., Majdani, O.: Characterizing the size of the target region for atraumatic opening of the cochlea through the facial recess. Computerized Medical Imaging and Graphics: The Official Jounal of the Computerized Medical Imaging Society 77, 101655 (2019)

  36. Weber, S., Gavaghan, K., Wimmer, W., Williamson, T., Gerber, N., Anso, J., Bell, B., Feldmann, A., Rathgeb, C., Matulic, M., Stebinger, M., Schneider, D., Mantokoudis, G., Scheidegger, O., Wagner, F., Kompis, M., Caversaccio, M.: Instrument flight to the inner ear. Sci, Robot (2017)

    Book  Google Scholar 

  37. Zhu, X., Liu, X., Lei, Z., Li, S.Z.: Face alignment in full pose range: A 3d total solution, arXiv e-prints (2018)

  38. Feng, Z.H., Kittler, J., Awais, M., Huber, P., Wu, X.J.: Wing loss for robust facial landmark localisation with convolutional neural networks (2017)

  39. Toshev, A., Szegedy, C.: Deeppose: Human pose estimation via deep neural networks, in: 2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), IEEE Conference on Computer Vision and Pattern Recognition (2014)

  40. Ge, L., Liang, H., Yuan, J., Thalmann, D.: 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

  41. Cramer, J., Wu, H.H., Salamon, J., Bello, J.P.: Look, listen, and learn more: Design choices for deep audio embeddings, in: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019)

  42. Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K.: Coarse-to-fine volumetric prediction for single-image 3d human pose, in: IEEE Conference on Computer Vision and Pattern Recognition (2017)

  43. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation (2018)

  44. Wu, Y., Ji, Q.: Facial landmark detection: A literature survey. International J. Comput. Vis. 127(2), 115–142 (2019)

    Article  Google Scholar 

  45. Khan, K., Khan, R.U., Leonardi, R., Migliorati, P., Benini, S.: Head pose estimation: A survey of the last ten years. Signal Processing: Image Communication 99, 116479 (2021)

    Google Scholar 

  46. Kartynnik, Y., Ablavatski, A., Grishchenko, I., Grundmann, M.: Real-time facial surface geometry from monocular video on mobile gpus, arXiv preprint arXiv:1907.06724 (2019)

  47. Fanelli, G., Dantone, M., Gall, J., Fossati, A., Van Gool, L.: Random forests for real time 3d face analysis. Int. J. Comput. Vis. 101(3), 437–458 (2013)

    Article  Google Scholar 

  48. Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S.Z.: Face alignment across large poses: A 3d solution, CoRR abs/1511.07212 (2015). arXiv:1511.07212. http://arxiv.org/abs/1511.07212

  49. Liu, H., Liu, F., Yu, H., Du, Z.: A robotic system integrated with CBCT for cochlear implant surgery: Accuracy improvement and validation. IEEE Robotics and Automation Letters, pp. 1–8 (2023). https://doi.org/10.1109/LRA.2023.3325778

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Acknowledgements

This research is supported by National Key Research and Development Program of China. We also acknowledge the comments of anonymous reviewers.

Funding

The National Key Research and Development Program of China (Project No.2019YFB1311800 and No.2019YFB1-311802).

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Authors

Contributions

Hengjia Liu: Conceptualization, Methodology, Software, Writing. Hongjian Yu: Project administration. Zhijiang Du: Supervision. Feng Liu: Software, Validation. Xuanbox Fan: Software, Validation, Writing. Lining Sun: Supervision

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Correspondence to Hongjian Yu.

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Our work was authorized and ethically reviewed by “The sixth Medical Center of PLA General Hospital” (Permitted NO.HZKY-PJ-2022-35).

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Liu, H., Yu, H., Du, Z. et al. Theory and Application of High-Precision Preoperative Positioning for Cochlear Surgical Robot. J Intell Robot Syst 109, 65 (2023). https://doi.org/10.1007/s10846-023-02001-2

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