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Deep Learning and Robotics, Surgical Robot Applications

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Artificial Intelligence for Robotics and Autonomous Systems Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1093))

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Abstract

Surgical robots can perform difficult tasks that humans cannot. They can perform repetitive tasks, work with hazardous materials, and can operate difficult objects. This has helped businesses, saved time and money while also preventing numerous accidents. The use of surgical robots, also known as robot-assisted surgery allows medical professionals to perform a wide range of complex procedures with greater accuracy, adaptability, and control than traditional methods. Minimally invasive surgery, which is frequently associated with robotic surgery, is performed through small incisions. It is also used in some traditional open surgical procedures. This chapter discusses advanced robotic surgical systems and deep learning (DL). The purpose of this chapter is to provide an overview of the major issues in artificial intelligence (AI), including how they apply to and limit surgical robots. Each surgical system is thoroughly explained in the chapter, along with any most recent AI-based improvements. Case studies are provided with the information on recent advancements and on the role of DL, and future surgical robotics applications in ophthalmology are also thoroughly discussed. The new ideas, comparisons, and updates on surgical robotics and deep learning are all summarized in this chapter.

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References

  1. Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2016). End-to-end training of deep visuomotor policies. The Journal of Machine Learning Research, 17(1), 1334–1373.

    MathSciNet  MATH  Google Scholar 

  2. Bakshi, G., Kumar, A., & Puranik, A. N. (2022). Adoption of robotics technology in healthcare sector. In Advances in communication, devices and networking (pp. 405–414). Singapore: Springer.

    Google Scholar 

  3. Maibaum, A., Bischof, A., Hergesell, J., & Lipp, B. (2022). A critique of robotics in health care. AI & Society, 37(2), 467–477.

    Article  Google Scholar 

  4. Tasioulas, J. (2019). First steps towards an ethics of robots and artificial intelligence. Journal of Practical Ethics, 7(1).

    Google Scholar 

  5. Hallevy, G. (2013). When robots kill: Artificial intelligence under criminal law. UPNE.

    Google Scholar 

  6. Bryndin, E. (2019). Robots with artificial intelligence and spectroscopic sight in hi-tech labor market. International Journal of Systems Science and Applied Mathematic, 4(3), 31–37.

    Article  Google Scholar 

  7. Lopes, V., Alexandre, L. A. & Pereira, N. (2019). Controlling robots using artificial intelligence and a consortium blockchain. arXiv:1903.00660.

  8. Bataev, A. V., Dedyukhina, N., & Nasrutdinov, M. N. (2020, February). Innovations in the financial sphere: performance evaluation of introducing service robots with artificial intelligence. In 2020 9th International Conference on Industrial Technology and Management (ICITM) (pp. 256–260). IEEE.

    Google Scholar 

  9. Nitto, H., Taniyama, D., & Inagaki, H. (2017). Social acceptance and impact of robots and artificial intelligence. Nomura Research Institute Papers, 211, 1–15.

    Google Scholar 

  10. Yoganandhan, A., Kanna, G. R., Subhash, S. D., & Jothi, J. H. (2021). Retrospective and prospective application of robots and artificial intelligence in global pandemic and epidemic diseases. Vacunas (English Edition), 22(2), 98–105.

    Article  Google Scholar 

  11. Rajan, K., & Saffiotti, A. (2017). Towards a science of integrated AI and Robotics. Artificial Intelligence, 247, 1–9.

    Article  MathSciNet  MATH  Google Scholar 

  12. Chatila, R., Renaudo, E., Andries, M., Chavez-Garcia, R. O., Luce-Vayrac, P., Gottstein, R., Alami, R., Clodic, A., Devin, S., Girard, B., & Khamassi, M. (2018). Toward self-aware robots. Frontiers in Robotics and AI, 5, 88.

    Article  Google Scholar 

  13. Gonzalez-Jimenez, H. (2018). Taking the fiction out of science fiction:(Self-aware) robots and what they mean for society, retailers and marketers. Futures, 98, 49–56.

    Article  Google Scholar 

  14. Schostek, S., Schurr, M. O., & Buess, G. F. (2009). Review on aspects of artificial tactile feedback in laparoscopic surgery. Medical Engineering & Physics, 31(8), 887–898.

    Article  Google Scholar 

  15. Naitoh, T., Gagner, M., Garcia-Ruiz, A., Heniford, B. T., Ise, H., & Matsuno, S. (1999). Hand-assisted laparoscopic digestive surgery provides safety and tactile sensation for malignancy or obesity. Surgical Endoscopy, 13(2), 157–160.

    Article  Google Scholar 

  16. Schostek, S., Ho, C. N., Kalanovic, D., & Schurr, M. O. (2006). Artificial tactile sensing in minimally invasive surgery–a new technical approach. Minimally Invasive Therapy & Allied Technologies, 15(5), 296–304.

    Article  Google Scholar 

  17. Kraft, B. M., Jäger, C., Kraft, K., Leibl, B. J., & Bittner, R. (2004). The AESOP robot system in laparoscopic surgery: Increased risk or advantage for surgeon and patient? Surgical Endoscopy And Other Interventional Techniques, 18(8), 1216–1223.

    Article  Google Scholar 

  18. Troisi, R. I., Patriti, A., Montalti, R., & Casciola, L. (2013). Robot assistance in liver surgery: A real advantage over a fully laparoscopic approach? Results of a comparative bi-institutional analysis. The International Journal of Medical Robotics and Computer Assisted Surgery, 9(2), 160–166.

    Article  Google Scholar 

  19. Dupont, P. E., Nelson, B. J., Goldfarb, M., Hannaford, B., Menciassi, A., O’Malley, M. K., Simaan, N., Valdastri, P., & Yang, G. Z. (2021). A decade retrospective of medical robotics research from 2010 to 2020. Science Robotics, 6(60), eabi8017.

    Google Scholar 

  20. Fuchs, K. H. (2002). Minimally invasive surgery. Endoscopy, 34(02), 154–159.

    Article  Google Scholar 

  21. Robinson, T. N., & Stiegmann, G. V. (2004). Minimally invasive surgery. Endoscopy, 36(01), 48–51.

    Article  Google Scholar 

  22. McDonald, G. J. (2021) Design and modeling of millimeter-scale soft robots for medical applications (Doctoral dissertation, University of Minnesota).

    Google Scholar 

  23. Currò, G., La Malfa, G., Caizzone, A., Rampulla, V., & Navarra, G. (2015). Three-dimensional (3D) versus two-dimensional (2D) laparoscopic bariatric surgery: A single-surgeon prospective randomized comparative study. Obesity Surgery, 25(11), 2120–2124.

    Article  Google Scholar 

  24. Dogangil, G., Davies, B. L., & Rodriguez, Y., & Baena, F. (2010) A review of medical robotics for minimally invasive soft tissue surgery. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 224(5), 653–679.

    Google Scholar 

  25. Yu, L., Wang, Z., Yu, P., Wang, T., Song, H., & Du, Z. (2014). A new kinematics method based on a dynamic visual window for a surgical robot. Robotica, 32(4), 571–589.

    Article  Google Scholar 

  26. Byrn, J. C., Schluender, S., Divino, C. M., Conrad, J., Gurland, B., Shlasko, E., & Szold, A. (2007). Three-dimensional imaging improves surgical performance for both novice and experienced operators using the da Vinci Robot System. The American Journal of Surgery, 193(4), 519–522.

    Article  Google Scholar 

  27. Kim, S., Chung, J., Yi, B. J., & Kim, Y. S. (2010). An assistive image-guided surgical robot system using O-arm fluoroscopy for pedicle screw insertion: Preliminary and cadaveric study. Neurosurgery, 67(6), 1757–1767.

    Article  Google Scholar 

  28. Nagy, T. D., & Haidegger, T. (2019). A dvrk-based framework for surgical subtask automation. Acta Polytechnica Hungarica (pp.61–78).

    Google Scholar 

  29. Millan, B., Nagpal, S., Ding, M., Lee, J. Y., & Kapoor, A. (2021). A scoping review of emerging and established surgical robotic platforms with applications in urologic surgery. Société Internationale d’Urologie Journal, 2(5), 300–310

    Google Scholar 

  30. Nagyné Elek, R., & Haidegger, T. (2019). Robot-assisted minimally invasive surgical skill assessment—Manual and automated platforms. Acta Polytechnica Hungarica, 16(8), 141–169.

    Google Scholar 

  31. Okamura, A. M. (2009). Haptic feedback in robot-assisted minimally invasive surgery. Current Opinion Urology, 19(1), 102.

    Article  Google Scholar 

  32. Bark, K., McMahan, W., Remington, A., Gewirtz, J., Wedmid, A., Lee, D. I., & Kuchenbecker, K. J. (2013). In vivo validation of a system for haptic feedback of tool vibrations in robotic surgery. Surgical Endoscopy, 27(2), 656–664.

    Article  Google Scholar 

  33. Van der Meijden, O. A., & Schijven, M. P. (2009). The value of haptic feedback in conventional and robot-assisted minimal invasive surgery and virtual reality training: A current review. Surgical Endoscopy, 23(6), 1180–1190.

    Article  Google Scholar 

  34. Bethea, B. T., Okamura, A. M., Kitagawa, M., Fitton, T. P., Cattaneo, S. M., Gott, V. L., Baumgartner, W. A., & Yuh, D. D. (2004). Application of haptic feedback to robotic surgery. Journal of Laparoendoscopic & Advanced Surgical Techniques, 14(3), 191–195.

    Article  Google Scholar 

  35. Amirabdollahian, F., Livatino, S., Vahedi, B., Gudipati, R., Sheen, P., Gawrie-Mohan, S., & Vasdev, N. (2018). Prevalence of haptic feedback in robot-mediated surgery: A systematic review of literature. Journal of robotic surgery, 12(1), 11–25.

    Article  Google Scholar 

  36. Okamura, A. M. (2004). Methods for haptic feedback in teleoperated robot-assisted surgery. Industrial Robot: An International Journal, 31(6), 499–508.

    Article  Google Scholar 

  37. Pacchierotti, C., Scheggi, S., Prattichizzo, D., & Misra, S. (2016). Haptic feedback for microrobotics applications: A review. Frontiers in Robotics and AI, 3, 53.

    Article  Google Scholar 

  38. Yeh, C. H., Su, F. C., Shan, Y. S., Dosaev, M., Selyutskiy, Y., Goryacheva, I., & Ju, M. S. (2020). Application of piezoelectric actuator to simplified haptic feedback system. Sensors and Actuators A: Physical, 303, 111820.

    Article  Google Scholar 

  39. Okamura, A. M., Dennerlein, J. T., & Howe, R. D. (1998, May). Vibration feedback models for virtual environments. In Proceedings of the 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146) (Vol. 1, pp. 674–679). IEEE.

    Google Scholar 

  40. Luostarinen, L. O., Åman, R., & Handroos, H. (2016, October). Haptic joystick for improving controllability of remote-operated hydraulic mobile machinery. In Fluid Power Systems Technology (Vol. 50473, p. V001T01A003). American Society of Mechanical Engineers.

    Google Scholar 

  41. Shang, W., Su, H., Li, G., & Fischer, G. S. (2013, November). Teleoperation system with hybrid pneumatic-piezoelectric actuation for MRI-guided needle insertion with haptic feedback. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4092–4098). IEEE.

    Google Scholar 

  42. Kim, P., Kim, S., Park, Y. D., & Choi, S. B. (2016). Force modeling for incisions into various tissues with MRF haptic master. Smart Materials and Structures, 25(3), 035008.

    Article  Google Scholar 

  43. Hooshiar, A., Payami, A., Dargahi, J., & Najarian, S. (2021). Magnetostriction-based force feedback for robot-assisted cardiovascular surgery using smart magnetorheological elastomers. Mechanical Systems and Signal Processing, 161, 107918.

    Article  Google Scholar 

  44. Shokrollahi, E., Goldenberg, A. A., Drake, J. M., Eastwood, K. W., & Kang, M. (2018, December). Application of a nonlinear Hammerstein-Wiener estimator in the development and control of a magnetorheological fluid haptic device for robotic bone biopsy. In Actuators (Vol. 7, No. 4, p. 83). MDPI.

    Google Scholar 

  45. Najmaei, N., Asadian, A., Kermani, M. R., & Patel, R. V. (2015). Design and performance evaluation of a prototype MRF-based haptic interface for medical applications. IEEE/ASME Transactions on Mechatronics, 21(1), 110–121.

    Google Scholar 

  46. Song, Y., Guo, S., Yin, X., Zhang, L., Wang, Y., Hirata, H., & Ishihara, H. (2018). Design and performance evaluation of a haptic interface based on MR fluids for endovascular tele-surgery. Microsystem Technologies, 24(2), 909–918.

    Article  Google Scholar 

  47. Kikuchi, T., Takano, T., Yamaguchi, A., Ikeda, A. and Abe, I. (2021, September). Haptic interface with twin-driven MR fluid actuator for teleoperation endoscopic surgery system. In Actuators (Vol. 10, No. 10, p. 245). MDPI.

    Google Scholar 

  48. Najmaei, N., Asadian, A., Kermani, M. R. & Patel, R. V. (2015, September). Performance evaluation of Magneto-Rheological based actuation for haptic feedback in medical applications. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 573–578). IEEE.

    Google Scholar 

  49. Gao, Q., Zhan, Y., Song, Y., Liu, J., & Wu, J. (2021, August). An MR fluid based master manipulator of the vascular intervention robot with haptic feedback. In 2021 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 158–163). IEEE.

    Google Scholar 

  50. Nguyen, N. D., Truong, T. D., Nguyen, D. H. & Nguyen, Q. H. (2019, March). Development of a 3D haptic spherical master manipulator based on MRF actuators. In Active and Passive Smart Structures and Integrated Systems XIII (Vol. 10967, pp. 431–440). SPIE.

    Google Scholar 

  51. Kim, S., Kim, P., Park, C. Y., & Choi, S. B. (2016). A new tactile device using magneto-rheological sponge cells for medical applications: Experimental investigation. Sensors and Actuators A: Physical, 239, 61–69.

    Article  Google Scholar 

  52. Cha, S. W., Kang, S. R., Hwang, Y. H., & Choi, S. B. (2017, April). A single of MR sponge tactile sensor design for medical applications. In Active and Passive Smart Structures and Integrated Systems (Vol. 10164, pp. 520–525). SPIE.

    Google Scholar 

  53. Oh, J. S., Sohn, J. W., & Choi, S. B. (2018). Material characterization of hardening soft sponge featuring MR fluid and application of 6-DOF MR haptic master for robot-assisted surgery. Materials, 11(8), 1268.

    Article  Google Scholar 

  54. Park, Y. J., & Choi, S. B. (2021). A new tactile transfer cell using magnetorheological materials for robot-assisted minimally invasive surgery. Sensors, 21(9), 3034.

    Article  Google Scholar 

  55. Park, Y. J., Yoon, J. Y., Kang, B. H., Kim, G. W., & Choi, S. B. (2020). A tactile device generating repulsive forces of various human tissues fabricated from magnetic-responsive fluid in porous polyurethane. Materials, 13(5), 1062.

    Article  Google Scholar 

  56. Park, Y. J., Lee, E. S., & Choi, S. B. (2022). A cylindrical grip type of tactile device using Magneto-Responsive materials integrated with surgical robot console: design and analysis. Sensors, 22(3), 1085.

    Article  Google Scholar 

  57. Martin, J. A., Regehr, G., Reznick, R., Macrae, H., Murnaghan, J., Hutchison, C., & Brown, M. (1997). Objective structured assessment of technical skill (OSATS) for surgical residents. British Journal of Surgery, 84(2), 273–278.

    Google Scholar 

  58. Vassiliou, M. C., Feldman, L. S., Andrew, C. G., Bergman, S., Leffondré, K., Stanbridge, D., & Fried, G. M. (2005). A global assessment tool for evaluation of intraoperative laparoscopic skills. The American Journal of Surgery, 190(1), 107–113.

    Article  Google Scholar 

  59. Goh, A. C., Goldfarb, D. W., Sander, J. C., Miles, B. J., & Dunkin, B. J. (2012). Global evaluative assessment of robotic skills: Validation of a clinical assessment tool to measure robotic surgical skills. The Journal of Urology, 187(1), 247–252.

    Article  Google Scholar 

  60. Insel, A., Carofino, B., Leger, R., Arciero, R., & Mazzocca, A. D. (2009). The development of an objective model to assess arthroscopic performance. JBJS, 91(9), 2287–2295.

    Article  Google Scholar 

  61. Champagne, B. J., Steele, S. R., Hendren, S. K., Bakaki, P. M., Roberts, P. L., Delaney, C. P., Brady, J. T., & MacRae, H. M. (2017). The American Society of Colon and Rectal Surgeons assessment tool for performance of laparoscopic colectomy. Diseases of the Colon & Rectum, 60(7), 738–744.

    Article  Google Scholar 

  62. Koehler, R. J., Amsdell, S., Arendt, E. A., Bisson, L. J., Bramen, J. P., Butler, A., Cosgarea, A. J., Harner, C. D., Garrett, W. E., Olson, T., & Warme, W. J. (2013). The arthroscopic surgical skill evaluation tool (ASSET). The American Journal of Sports Medicine, 41(6), 1229–1237.

    Article  Google Scholar 

  63. Shademan, A., Decker, R. S., Opfermann, J. D., Leonard, S., Krieger, A., & Kim, P. C. (2016). Supervised autonomous robotic soft tissue surgery. Science Translational Medicine, 8(337), 337ra64–337ra64.

    Google Scholar 

  64. Garrow, C. R., Kowalewski, K. F., Li, L., Wagner, M., Schmidt, M. W., Engelhardt, S., Hashimoto, D. A., Kenngott, H. G., Bodenstedt, S., Speidel, S., & Mueller-Stich, B. P. (2021). Machine learning for surgical phase recognition: A systematic review. Annals of Surgery, 273(4), 684–693.

    Article  Google Scholar 

  65. Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920–1930.

    Article  Google Scholar 

  66. Lee, C. K., Hofer, I., Gabel, E., Baldi, P., & Cannesson, M. (2018). Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality. Anesthesiology, 129(4), 649–662.

    Article  Google Scholar 

  67. Maier-Hein, L., Vedula, S. S., Speidel, S., Navab, N., Kikinis, R., Park, A., Eisenmann, M., Feussner, H., Forestier, G., Giannarou, S., & Hashizume, M. (2017). Surgical data science for next-generation interventions. Nature Biomedical Engineering, 1(9), 691–696.

    Article  Google Scholar 

  68. Maier-Hein, L., Eisenmann, M., Sarikaya, D., März, K., Collins, T., Malpani, A., Fallert, J., Feussner, H., Giannarou, S., Mascagni, P., & Nakawala, H. (2022). Surgical data science–from concepts toward clinical translation. Medical Image Analysis, 76, 102306.

    Article  Google Scholar 

  69. Kosak, O., Wanninger, C., Angerer, A., Hoffmann, A., Schiendorfer, A., & Seebach, H. (2016, September). Towards self-organizing swarms of reconfigurable self-aware robots. In 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS* W) (pp. 204–209). IEEE.

    Google Scholar 

  70. Pierson, H. A., & Gashler, M. S. (2017). Deep learning in robotics: A review of recent research. Advanced Robotics, 31(16), 821–835.

    Article  Google Scholar 

  71. Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M., & Corke, P. (2018). The limits and potentials of deep learning for robotics. The International Journal of Robotics Research, 37(4–5), 405–420.

    Article  Google Scholar 

  72. Miyajima, R. (2017). Deep learning triggers a new era in industrial robotics. IEEE Multimedia, 24(4), 91–96.

    Article  MathSciNet  Google Scholar 

  73. Degrave, J., Hermans, M., & Dambre, J. (2019) A differentiable physics engine for deep learning in robotics. Frontiers in Neurorobotics, 6.

    Google Scholar 

  74. Károly, A. I., Galambos, P., Kuti, J., & Rudas, I. J. (2020). Deep learning in robotics: Survey on model structures and training strategies. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(1), 266–279.

    Article  Google Scholar 

  75. Mouha, R. A. (2021). Deep learning for robotics. Journal of Data Analysis and Information Processing, 9(02), 63.

    Article  Google Scholar 

  76. Morales, E. F., Murrieta-Cid, R., Becerra, I., & Esquivel-Basaldua, M. A. (2021). A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning. Intelligent Service Robotics, 14(5), 773–805.

    Article  Google Scholar 

  77. McLaughlin, E., Charron, N., & Narasimhan, S. (2020). Automated defect quantification in concrete bridges using robotics and deep learning. Journal of Computing in Civil Engineering, 34(5), 04020029.

    Article  Google Scholar 

  78. Rassweiler, J. J., Autorino, R., Klein, J., Mottrie, A., Goezen, A. S., Stolzenburg, J. U., Rha, K. H., Schurr, M., Kaouk, J., Patel, V., & Dasgupta, P. (2017). Future of robotic surgery in urology. BJU International, 120(6), 822–841.

    Article  Google Scholar 

  79. Chang, K. D., Abdel Raheem, A., Choi, Y. D., Chung, B. H., & Rha, K. H. (2018). Retzius-sparing robot-assisted radical prostatectomy using the Revo-i robotic surgical system: Surgical technique and results of the first human trial. BJU International, 122(3), 441–448.

    Article  Google Scholar 

  80. Chen, J., Oh, P. J., Cheng, N., Shah, A., Montez, J., Jarc, A., Guo, L., Gill, I. S., & Hung, A. J. (2018). Use of automated performance metrics to measure surgeon performance during robotic vesicourethral anastomosis and methodical development of a training tutorial. The Journal of Urology, 200(4), 895–902.

    Article  Google Scholar 

  81. Burgner-Kahrs, J., Rucker, D. C., & Choset, H. (2015). Continuum robots for medical applications: A survey. IEEE Transactions on Robotics, 31(6), 1261–1280.

    Article  Google Scholar 

  82. Münzer, B., Schoeffmann, K., & Böszörmenyi, L. (2018). Content-based processing and analysis of endoscopic images and videos: A survey. Multimedia Tools and Applications, 77(1), 1323–1362.

    Article  Google Scholar 

  83. Speidel, S., Delles, M., Gutt, C., & Dillmann, R. (2006, August). Tracking of instruments in minimally invasive surgery for surgical skill analysis. In International Workshop on Medical Imaging and Virtual Reality (pp. 148–155). Berlin, Heidelberg: Springer.

    Google Scholar 

  84. Doignon, C., Nageotte, F., & Mathelin, M. D. (2006). Segmentation and guidance of multiple rigid objects for intra-operative endoscopic vision. In Dynamical Vision (pp. 314–327). Berlin, Heidelberg: Springer.

    Google Scholar 

  85. Pezzementi, Z., Voros, S., & Hager, G. D. (2009, May). Articulated object tracking by rendering consistent appearance parts. In 2009 IEEE International Conference on Robotics and Automation (pp. 3940–3947). IEEE.

    Google Scholar 

  86. Bouget, D., Benenson, R., Omran, M., Riffaud, L., Schiele, B., & Jannin, P. (2015). Detecting surgical tools by modelling local appearance and global shape. IEEE Transactions on Medical Imaging, 34(12), 2603–2617.

    Article  Google Scholar 

  87. Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P. M., Zietz, M., Hoffman, M. M., & Xie, W. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387.

    Article  Google Scholar 

  88. Kalinin, A. A., Higgins, G. A., Reamaroon, N., Soroushmehr, S., Allyn-Feuer, A., Dinov, I. D., Najarian, K., & Athey, B. D. (2018). Deep learning in pharmacogenomics: From gene regulation to patient stratification. Pharmacogenomics, 19(7), 629–650.

    Article  Google Scholar 

  89. Yong, C. W., Teo, K., Murphy, B. P., Hum, Y. C., Tee, Y. K., Xia, K., & Lai, K. W. (2021). Knee osteoarthritis severity classification with ordinal regression module. Multimedia Tools and Applications, 1–13.

    Google Scholar 

  90. Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., & Saarakkala, S. (2018). Automatic knee osteoarthritis diagnosis from plain radiographs: A deep learning-based approach. Scientific Reports, 8(1), 1–10.

    Article  Google Scholar 

  91. Iglovikov, V. I., Rakhlin, A., Kalinin, A. A., & Shvets, A.A. (2018). Paediatric bone age assessment using deep convolutional neural networks. In Deep learning in medical image analysis and multimodal learning for clinical decision support (pp. 300–308). Cham: Springer.

    Google Scholar 

  92. Garcia-Peraza-Herrera, L. C., Li, W., Fidon, L., Gruijthuijsen, C., Devreker, A., Attilakos, G., Deprest, J., Vander Poorten, E., Stoyanov, D., Vercauteren, T., & Ourselin, S. (2017, September). Toolnet: holistically-nested real-time segmentation of robotic surgical tools. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5717–5722). IEEE.

    Google Scholar 

  93. Attia, M., Hossny, M., Nahavandi, S., & Asadi, H. (2017, October). Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 3373–3378). IEEE.

    Google Scholar 

  94. Pakhomov, D., Premachandran, V., Allan, M., Azizian, M., & Navab, N. (2019, October). Deep residual learning for instrument segmentation in robotic surgery. In International Workshop on Machine Learning in Medical Imaging (pp. 566–573). Cham: Springer.

    Google Scholar 

  95. Solovyev, R., Kustov, A., Telpukhov, D., Rukhlov, V., & Kalinin, A. (2019, January). Fixed-point convolutional neural network for real-time video processing in FPGA. In 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) (pp. 1605–1611). IEEE.

    Google Scholar 

  96. Shvets, A. A., Rakhlin, A., Kalinin, A. A., & Iglovikov, V. I. (2018, December). Automatic instrument segmentation in robot-assisted surgery using deep learning. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 624–628). IEEE.

    Google Scholar 

  97. Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 234–241). Cham: Springer.

    Google Scholar 

  98. Hamad, G. G., & Curet, M. (2010). Minimally invasive surgery. The American Journal of Surgery, 199(2), 263–265.

    Article  Google Scholar 

  99. Phee, S. J., Low, S. C., Huynh, V. A., Kencana, A. P., Sun, Z. L. & Yang, K. (2009, September). Master and slave transluminal endoscopic robot (MASTER) for natural orifice transluminal endoscopic surgery. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1192–1195). IEEE.

    Google Scholar 

  100. Wang, Z., Sun, Z., & Phee, S. J. (2013). Haptic feedback and control of a flexible surgical endoscopic robot. Computer Methods and Programs in Biomedicine, 112(2), 260–271.

    Article  Google Scholar 

  101. Ehrampoosh, S., Dave, M., Kia, M. A., Rablau, C., & Zadeh, M. H. (2013). Providing haptic feedback in robot-assisted minimally invasive surgery: A direct optical force-sensing solution for haptic rendering of deformable bodies. Computer Aided Surgery, 18(5–6), 129–141.

    Article  Google Scholar 

  102. Akinbiyi, T., Reiley, C. E., Saha, S., Burschka, D., Hasser, C. J., Yuh, D .D. & Okamura, A. M. (2006, September). Dynamic augmented reality for sensory substitution in robot-assisted surgical systems. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 567–570). IEEE.

    Google Scholar 

  103. Tavakoli, M., Aziminejad, A., Patel, R. V., & Moallem, M. (2006). Methods and mechanisms for contact feedback in a robot-assisted minimally invasive environment. Surgical Endoscopy and Other Interventional Techniques, 20(10), 1570–1579.

    Article  Google Scholar 

  104. Hayward, V., Astley, O. R., Cruz‐Hernandez, M., Grant, D., & Robles‐De‐La‐Torre, G. (2004). Haptic interfaces and devices. Sensor Review.

    Google Scholar 

  105. Rosen, J., Hannaford, B., MacFarlane, M. P., & Sinanan, M. N. (1999). Force controlled and teleoperated endoscopic grasper for minimally invasive surgery-experimental performance evaluation. IEEE Transactions on Biomedical Engineering, 46(10), 1212–1221.

    Article  Google Scholar 

  106. Tholey, G., Pillarisetti, A., Green, W., & Desai, J. P. (2004, June). Design, development, and testing of an automated laparoscopic grasper with 3-D force measurement capability. In International Symposium on Medical Simulation (pp. 38–48). Berlin, Heidelberg: Springer.

    Google Scholar 

  107. Tadano, K., & Kawashima, K. (2010). Development of a master–slave system with force-sensing abilities using pneumatic actuators for laparoscopic surgery. Advanced Robotics, 24(12), 1763–1783.

    Article  Google Scholar 

  108. Valdastri, P., Harada, K., Menciassi, A., Beccai, L., Stefanini, C., Fujie, M., & Dario, P. (2006). Integration of a miniaturised triaxial force sensor in a minimally invasive surgical tool. IEEE Transactions on Biomedical Engineering, 53(11), 2397–2400.

    Article  Google Scholar 

  109. Howe, R. D., Peine, W. J., Kantarinis, D. A., & Son, J. S. (1995). Remote palpation technology. IEEE Engineering in Medicine and Biology Magazine, 14(3), 318–323.

    Article  Google Scholar 

  110. Ohtsuka, T., Furuse, A., Kohno, T., Nakajima, J., Yagyu, K., & Omata, S. (1995). Application of a new tactile sensor to thoracoscopic surgery: Experimental and clinical study. The Annals of Thoracic Surgery, 60(3), 610–614.

    Article  Google Scholar 

  111. Lai, W., Cao, L., Xu, Z., Phan, P. T., Shum, P., & Phee, S. J. (2018, May). Distal end force sensing with optical fiber bragg gratings for tendon-sheath mechanisms in flexible endoscopic robots. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5349–5255). IEEE.

    Google Scholar 

  112. Kaneko, M., Wada, M., Maekawa, H., & Tanie, K. (1991, January). A new consideration on tendon-tension control system of robot hands. In Proceedings of the 1991 IEEE International Conference on Robotics and Automation (pp. 1028–1029). IEEE Computer Society.

    Google Scholar 

  113. Lampaert, V., Swevers, J., & Al-Bender, F. (2002). Modification of the Leuven integrated friction model structure. IEEE Transactions on Automatic Control, 47(4), 683–687.

    Article  MathSciNet  MATH  Google Scholar 

  114. Piatkowski, T. (2014). Dahl and LuGre dynamic friction models—The analysis of selected properties. Mechanism and Machine Theory, 73, 91–100.

    Article  Google Scholar 

  115. Do, T. N., Tjahjowidodo, T., Lau, M. W. S., & Phee, S. J. (2015). Nonlinear friction modelling and compensation control of hysteresis phenomena for a pair of tendon-sheath actuated surgical robots. Mechanical Systems and Signal Processing, 60, 770–784.

    Article  Google Scholar 

  116. Dinh, B. K., Cappello, L., Xiloyannis, M., & Masia, L. Position control using adaptive backlash.

    Google Scholar 

  117. Dinh, B. K., Cappello, L., Xiloyannis, M., & Masia, L. (2016, October). Position control using adaptive backlash compensation for bowden cable transmission in soft wearable exoskeleton. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5670–5676). IEEE.

    Google Scholar 

  118. Do, T. N., Tjahjowidodo, T., Lau, M. W. S., & Phee, S. J. (2014). An investigation of friction-based tendon sheath model appropriate for control purposes. Mechanical Systems and Signal Processing, 42(1–2), 97–114.

    Article  Google Scholar 

  119. Do, T. N., Tjahjowidodo, T., Lau, M. W. S., & Phee, S. J. (2015). A new approach of friction model for tendon-sheath actuated surgical systems: Nonlinear modelling and parameter identification. Mechanism and Machine Theory, 85, 14–24.

    Article  Google Scholar 

  120. Do, T. N., Tjahjowidodo, T., Lau, M. W. S., Yamamoto, T., & Phee, S. J. (2014). Hysteresis modeling and position control of tendon-sheath mechanism in flexible endoscopic systems. Mechatronics, 24(1), 12–22.

    Article  Google Scholar 

  121. Lenz, I., Lee, H., & Saxena, A. (2015). Deep learning for detecting robotic grasps. The International Journal of Robotics Research, 34(4–5), 705–724.

    Article  Google Scholar 

  122. Tan, Y. P., Liverneaux, P., & Wong, J. K. (2018). Current limitations of surgical robotics in reconstructive plastic microsurgery. Frontiers in surgery, 5, 22.

    Google Scholar 

  123. Longmore, S. K., Naik, G., & Gargiulo, G. D. (2020). Laparoscopic robotic surgery: Current perspective and future directions. Robotics, 9(2), 42.

    Article  Google Scholar 

  124. Camarillo, D. B., Krummel, T. M., & Salisbury, J. K., Jr. (2004). Robotic technology in surgery: Past, present, and future. The American Journal of Surgery, 188(4), 2–15.

    Article  Google Scholar 

  125. Kim, S. S., Dohler, M., & Dasgupta, P. (2018). The Internet of Skills: Use of fifth-generation telecommunications, haptics and artificial intelligence in robotic surgery. BJU International, 122(3), 356–358.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, Women University of AJ&K, Bagh, Pakistan

    Muhammad Shahid Iqbal & Fouzia Sher Akbar

  2. Anhui Polytechnic University Hefei, Wuhu, China

    Rashid Abbasi

  3. Higher Education Department Govt, AJ&K, Mirpur, 10250, Pakistan

    Waqas Ahmad

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  1. Muhammad Shahid Iqbal
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Correspondence to Muhammad Shahid Iqbal .

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  1. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

  2. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Anis Koubaa

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Iqbal, M.S., Abbasi, R., Ahmad, W., Akbar, F.S. (2023). Deep Learning and Robotics, Surgical Robot Applications. In: Azar, A.T., Koubaa, A. (eds) Artificial Intelligence for Robotics and Autonomous Systems Applications. Studies in Computational Intelligence, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-031-28715-2_6

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