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Robotic Automation for Surgery

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Digital Surgery

Abstract

The paradigm shift in surgery towards progressively more minimally invasive approach in the past three decades represents significantly underappreciated transition and opportunity from analogue to a digital era. Despite the concerted efforts in robotic-assisted surgery over the last two decades, less than 10% of the overall soft tissue surgery in the USA and less than 0.5% of all surgery globally are currently performed using surgical robots today. Although cost and utilization are often cited as the major challenge to a broader adoption of the technology, a new paradigm of surgical vision and intelligence driving automation and autonomy, initially at subtask and then task levels but eventually at systems level, is necessary and promises an enhanced adoption of the technology with measurable metrics on improved outcome, safety and accessibility. The goal of these inevitable changes should be viewed not just an ancillary technology that extends the human surgeon’s ability to approved level of competency to practice clinically but potentially an enabling technology beyond human dexterity into a digital intelligence that could potentially expand surgeon’s capacity and capability to a level of digitally accessible proficiency.

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References

  1. Intuitive Surgical Upside If da Vinci Systems Reaches 25% Share In Global Surgical Robots Market? [Internet]. [cited 2019 Oct 26]. Available from: https://www.forbes.com/sites/greatspeculations/2019/03/27/intuitive-surgical-upside-if-da-vinci-systems-reaches-25-share-in-global-surgical-robots-market/#6f29e66c4fac.

  2. Simaan N, Yasin RM, Wang L. Medical technologies and challenges of robot-assisted minimally invasive intervention and diagnostics. Annu Rev Control Robot Auton Syst. 2018;1(1):465–90.

    Article  Google Scholar 

  3. Fletcher SA, Cole AP, Berg S, Pucheril D, Trinh Q-D. Adoption of robotic surgery: driven by market competition or a desire to improve patient care? Lancet Oncol. 2018;19(2):e66.

    Article  PubMed  Google Scholar 

  4. Smyth JK, Deveney KE, Sade RM. Who should adopt robotic surgery, and when? Ann Thorac Surg. 2013;96(4):1132–7.

    Article  PubMed  Google Scholar 

  5. Patel V. The 10th Society of Robotic Surgery meeting. 2019.

    Google Scholar 

  6. Yu H-Y, Friedlander DF, Patel S, Hu JC. The current status of robotic oncologic surgery. CA Cancer J Clin. 2013;63(1):45–56.

    Article  PubMed  Google Scholar 

  7. Moustris GP, Hiridis SC, Deliparaschos KM, Konstantinidis KM. Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature. Int J Med Robot Comput Assist Surg MRCAS. 2011;7(4):375–92.

    Article  CAS  Google Scholar 

  8. Adler JR, Chang SD, Murphy MJ, Doty J, Geis P, Hancock SL. The Cyberknife: a frameless robotic system for radiosurgery. Stereotact Funct Neurosurg. 1997;69(1–4 Pt 2):124–8.

    Article  PubMed  Google Scholar 

  9. Eranki A, Srinivasan P, Ries M, Kim A, Lazarski CA, Rossi CT, et al. High intensity focused ultrasound (HIFU) triggers immune sensitization of refractory murine neuroblastoma to checkpoint inhibitor therapy. Clin Cancer Res Off J Am Assoc Cancer Res. 2020;26(5):1152.

    Article  CAS  Google Scholar 

  10. Haidegger T. Autonomy for surgical robots: concepts and paradigms. IEEE Trans Med Robot Bionics. 2019;1(2):65–76.

    Article  Google Scholar 

  11. Shademan A, Decker RS, Opfermann JD, Leonard S, Krieger A, Kim PCW. Supervised autonomous robotic soft tissue surgery. Sci Transl Med. 2016;8(337):337ra64.

    Article  PubMed  Google Scholar 

  12. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70–6.

    Article  PubMed  Google Scholar 

  13. Maier-Hein L, Vedula SS, Speidel S, Navab N, Kikinis R, Park A, et al. Surgical data science for next-generation interventions. Nat Biomed Eng. 2017;1(9):691–6.

    Article  PubMed  Google Scholar 

  14. Hung AJ, Chen J, Gill IS. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg. 2018;153(8):770–1.

    Article  PubMed  Google Scholar 

  15. Chen J, Cheng N, Cacciamani G, Oh P, Lin-Brande M, Remulla D, et al. Objective assessment of robotic surgical technical skill: a systematic review. J Urol. 2019;201(3):461–9.

    Article  PubMed  Google Scholar 

  16. Birkmeyer JD, Finks JF, O’Reilly A, Oerline M, Carlin AM, Nunn AR, et al. Surgical skill and complication rates after bariatric surgery. N Engl J Med. 2013;369(15):1434–42.

    Article  CAS  PubMed  Google Scholar 

  17. Ghasem A, Sharma A, Greif DN, Alam M, Maaieh MA. The arrival of robotics in spine surgery: a review of the literature. Spine. 2018;43(23):1670–7.

    Article  PubMed  Google Scholar 

  18. Yang G-Z, Bellingham J, Dupont PE, Fischer P, Floridi L, Full R, et al. The grand challenges of Science Robotics. Sci Robot. 2018;3(14):eaar7650.

    Article  PubMed  Google Scholar 

  19. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

    Article  CAS  PubMed  Google Scholar 

  20. Pakhomov D, Premachandran V, Allan M, Azizian M, Navab N. Deep residual learning for instrument segmentation in robotic surgery. In: Suk H-I, Liu M, Yan P, Lian C, editors. Machine learning in medical imaging. Cham: Springer International Publishing; 2019. p. 566–73. (Lecture Notes in Computer Science).

    Chapter  Google Scholar 

  21. Nguyen TT, Nguyen ND, Bello F, Nahavandi S. A new tensioning method using deep reinforcement learning for surgical pattern cutting. In: 2019 IEEE International Conference on Industrial Technology ICIT. 2019. p. 1339–44.

    Google Scholar 

  22. Bohn R. The development of intelligent systems for industrial use: an empirical investigation. In: Research on technological innovation, management, and policy. Greenwich: J.A.I; 1986. p. 169–211.

    Google Scholar 

  23. Paxton C, Barnoy Y, Katyal K, Arora R, Hager GD. Visual robot task planning. In: 2019 international conference on robotics and automation (ICRA). 2019. p. 8832–8.

    Google Scholar 

  24. Dehghani H, Farritor S, Oleynikov D, Terry B. Automation of suturing path generation for da Vinci-Like surgical robotic systems. In: American Society of Mechanical Engineers Digital Collection; 2018 [cited 2019 Oct 22]. Available from: https://asmedigitalcollection.asme.org/BIOMED/proceedings/DMD2018/40789/V001T07A008/271978.

  25. Sen S, Garg A, Gealy DV, McKinley S, Jen Y, Goldberg K. Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization. In: 2016 IEEE international conference on robotics and automation (ICRA). 2016. p. 4178–85.

    Google Scholar 

  26. National Motor Vehicle Crash Causation Survey: Report to Congress. :47.

    Google Scholar 

  27. THE ROAD AHEAD National Highway Traffic Safety Administration Strategic Plan 2016–2020.

    Google Scholar 

  28. Dehghani Ashkezari H, Mirbagheri A, Farahmand F, Behzadipour S, Firoozbakhsh K. Real time simulation of grasping procedure of large internal organs during laparoscopic surgery. In: 2012 annual international conference of the IEEE engineering in medicine and biology society. 2012. p. 924–7.

    Google Scholar 

  29. Dehghani Ashkezari H, Mirbagheri A, Behzadipour S, Farahmand F. A mass-spring-damper model for real time simulation of the frictional grasping interactions between surgical tools and large organs. Sci Iran Int J Sci Technol. 2015;22(5):1833–41.

    Google Scholar 

  30. Heredia-Pérez SA, Harada K, Padilla-Castañeda MA, Marques-Marinho M, Márquez-Flores JA, Mitsuishi M. Virtual reality simulation of robotic transsphenoidal brain tumor resection: evaluating dynamic motion scaling in a master-slave system. Int J Med Robot [Internet]. 2019 Feb [cited 2019 Oct 22];15(1). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6587960/.

  31. Prasad SM, Prasad SM, Maniar HS, Chu C, Schuessler RB, Damiano RJ. Surgical robotics: impact of motion scaling on task performance. J Am Coll Surg. 2004;199(6):863–8.

    Article  PubMed  Google Scholar 

  32. Yuen SG, Kettler DT, Novotny PM, Plowes RD, Howe RD. Robotic motion compensation for beating heart intracardiac surgery. Int J Robot Res. 2009;28(10):1355–72.

    Article  Google Scholar 

  33. Lindgren K, Huang K, Hannaford B. Towards real-time surface tracking and motion compensation integration for robotic surgery. In: 2017 IEEE/SICE international symposium on system integration (SII). 2017. p. 450–6.

    Google Scholar 

  34. MacLachlan RA, Becker BC, Tabarés JC, Podnar GW, Lobes LA Jr, Riviere CN. Micron: an actively stabilized handheld tool for microsurgery. IEEE Trans Robot. 2012;28(1):195–212.

    Article  PubMed  Google Scholar 

  35. Heeman W, Steenbergen W, van Dam GM, Boerma EC. Clinical applications of laser speckle contrast imaging: a review. J Biomed Opt. 2019;24(8):080901.

    Article  CAS  PubMed Central  Google Scholar 

  36. Son GM, Kwon MS, Kim Y, Kim J, Kim SH, Lee JW. Quantitative analysis of colon perfusion pattern using indocyanine green (ICG) angiography in laparoscopic colorectal surgery. Surg Endosc. 2019;33(5):1640–9.

    Article  PubMed  Google Scholar 

  37. Schwaitzberg SD, Scott DJ, Jones DB, McKinley SK, Castrillion J, Hunter TD, et al. Threefold increased bile duct injury rate is associated with less surgeon experience in an insurance claims database: more rigorous training in biliary surgery may be needed. Surg Endosc. 2014;28(11):3068–73.

    Article  PubMed  Google Scholar 

  38. Zuzak KJ, Naik SC, Alexandrakis G, Hawkins D, Behbehani K, Livingston EH. Characterization of a near-infrared laparoscopic hyperspectral imaging system for minimally invasive surgery. Anal Chem. 2007;79(12):4709–15.

    Article  CAS  PubMed  Google Scholar 

  39. Yoon J, Joseph J, Waterhouse DJ, Luthman AS, Gordon GSD, di Pietro M, et al. A clinically translatable hyperspectral endoscopy (HySE) system for imaging the gastrointestinal tract. Nat Commun. 2019;10(1):1–13.

    Article  Google Scholar 

  40. Stroh M, Zimmer JP, Duda DG, Levchenko TS, Cohen KS, Brown EB, et al. Quantum dots spectrally distinguish multiple species within the tumor milieu in vivo. Nat Med. 2005;11(6):678–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Huber-Mörk R, Štolc S, Soukup D, Holländer B. Shape from refocus. In: Bebis G, Boyle R, Parvin B, Koracin D, McMahan R, Jerald J, et al., editors. Advances in visual computing. Cham: Springer International Publishing; 2014. p. 153–62. (Lecture Notes in Computer Science).

    Chapter  Google Scholar 

  42. Mirota DJ, Ishii M, Hager GD. Vision-based navigation in image-guided interventions. Annu Rev Biomed Eng. 2011;13:297–319.

    Article  CAS  PubMed  Google Scholar 

  43. Nguyen T-N, Huynh H-H, Meunier J. 3D reconstruction with time-of-flight depth camera and multiple mirrors. IEEE Access. 2018;6:38106–14.

    Article  Google Scholar 

  44. Maier-Hein L, Mountney P, Bartoli A, Elhawary H, Elson D, Groch A, et al. Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery. Med Image Anal. 2013;17(8):974–96.

    Article  CAS  PubMed  Google Scholar 

  45. Barron JT, Malik J. Shape, illumination, and reflectance from shading. IEEE Trans Pattern Anal Mach Intell. 2015;37(8):1670–87.

    Article  PubMed  Google Scholar 

  46. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2(1):35.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Eric Topol on the Future of Medicine. Wall Street Journal [Internet]. 2014 Jul 7 [cited 2019 Oct 26]; Available from: https://www.wsj.com/articles/eric-topol-on-the-future-of-medicine-1404765024.

  49. Sutton RS, Barto AG. Reinforcement learning: an introduction. MIT Press; 2018. 549 p.

    Google Scholar 

  50. [1902.01459] Autonomous tissue manipulation via surgical robot using learning based model predictive control [Internet]. [cited 2019 Oct 21]. Available from: https://arxiv.org/abs/1902.01459.

  51. Sangiovanni B, Rendiniello A, Incremona GP, Ferrara A, Piastra M. Deep reinforcement learning for collision avoidance of robotic manipulators. In: 2018 European Control Conference (ECC). 2018. p. 2063–8.

    Google Scholar 

  52. Kassahun Y, Yu B, Tibebu AT, Stoyanov D, Giannarou S, Metzen JH, et al. Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions. Int J Comput Assist Radiol Surg. 2016;11(4):553–68.

    Article  PubMed  Google Scholar 

  53. De Momi E, Kranendonk L, Valenti M, Enayati N, Ferrigno G. A neural network-based approach for trajectory planning in robot–human handover tasks. Front Robot AI [Internet]. 2016 [cited 2019 Oct 22];3. Available from: https://www.frontiersin.org/articles/10.3389/frobt.2016.00034/full.

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Author information

Authors and Affiliations

  1. Activ Surgical, Boston, MA, USA

    Hossein Dehghani

  2. Department of Surgery, Brown University; Active Surgical, Boston, MA, USA

    Peter C. W. Kim

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  1. Hossein Dehghani
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  2. Peter C. W. Kim
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Correspondence to Peter C. W. Kim .

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  1. Orlando, FL, USA

    Sam Atallah

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Dehghani, H., Kim, P.C.W. (2021). Robotic Automation for Surgery. In: Atallah, S. (eds) Digital Surgery. Springer, Cham. https://doi.org/10.1007/978-3-030-49100-0_15

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  • DOI: https://doi.org/10.1007/978-3-030-49100-0_15

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