Advertisement

Biomedical Microdevices

, 21:10 | Cite as

Biaxial sensing suture breakage warning system for robotic surgery

  • Yuan Dai
  • A. Abiri
  • J. Pensa
  • S. Liu
  • O. Paydar
  • H. Sohn
  • S. Sun
  • P. A. Pellionisz
  • C. Pensa
  • E. P. Dutson
  • W. S. Grundfest
  • R. N. CandlerEmail author
Article
  • 45 Downloads

Abstract

The number of procedures performed with robotic surgery may exceed one million globally in 2018. The continual lack of haptic feedback, however, forces surgeons to rely on visual cues in order to avoid breaking sutures due to excessive applied force. To mitigate this problem, the authors developed and validated a novel grasper-integrated system with biaxial shear sensing and haptic feedback to warn the operator prior to anticipated suture breakage. Furthermore, the design enables facile suture manipulation without a degradation in efficacy, as determined via measured tightness of resulting suture knots. Biaxial shear sensors were integrated with a da Vinci robotic surgical system. Novice subjects (n = 17) were instructed to tighten 10 knots, five times with the Haptic Feedback System (HFS) enabled, five times with the system disabled. Seven suture failures occurred in trials with HFS enabled while seventeen occurred in trials without feedback. The biaxial shear sensing system reduced the incidence of suture failure by 59% (p = 0.0371). It also resulted in 25% lower average applied force in comparison to trials without feedback (p = 0.00034), which is relevant because average force was observed to play a role in suture breakage (p = 0.03925). An observed 55% decrease in standard deviation of knot quality when using the HFS also indicates an improvement in consistency when using the feedback system. These results suggest this system may improve outcomes related to knot tying tasks in robotic surgery and reduce instances of suture failure while not degrading the quality of knots produced.

Keywords

Haptic feedback Shear sensor Robotic surgery Force sensor Haptics Da Vinci 

Notes

References

  1. A. Abiri, O. Paydar, A. Tao, M. LaRocca, K. Liu, B. Genovese, et al., Tensile strength and failure load of sutures for robotic surgery. Surg. Endosc. 31, 3258–3270 (Aug 2017)CrossRefGoogle Scholar
  2. A. Abiri, S. J. Askari, A. Tao, Y. Y. Juo, Y. Dai, J. Pensa, et al., “Suture breakage warning system for robotic surgery,” IEEE Trans. Biomed. Eng. (Sep 2018).  https://doi.org/10.1109/TBME.2018.2869417
  3. R. Anup, K.A. Balasubramanian, Surgical stress and the gastrointestinal tract. J. Surg. Res. 92, 291–300 (Aug 2000)CrossRefGoogle Scholar
  4. N.Q. Balaban, U.S. Schwarz, D. Riveline, P. Goichberg, G. Tzur, I. Sabanay, et al., Force and focal adhesion assembly: A close relationship studied using elastic micropatterned substrates. Nat. Cell Biol. 3, 466–472 (May 2001)CrossRefGoogle Scholar
  5. G.I. Barbash, B. Friedman, S.A. Glied, C.A. Steiner, Factors associated with adoption of robotic surgical technology in US hospitals and relationship to radical prostatectomy procedure volume. Ann. Surg. 259, 1–6 (Jan 2014)CrossRefGoogle Scholar
  6. V. Bhatia, R.K. Tandon, Stress and the gastrointestinal tract. J. Gastroenterol. Hepatol. 20, 332–339 (2005)CrossRefGoogle Scholar
  7. J.A. Cartmill, A.J. Shakeshaft, W.R. Walsh, C.J. Martin, High pressures are generated at the tip of laparoscopic graspers. ANZ J. Surg. 69, 127–130 (1999)CrossRefGoogle Scholar
  8. R. S. Dahiya and M. Valle, "Robotic Tactile Sensing Technologies and Systems," 2013Google Scholar
  9. S. De, J. Rosen, A. Dagan, B. Hannaford, P. Swanson, M. Sinanan, Assessment of tissue damage due to mechanical stresses. Int. J. Rob. Res. 26, 1159–1171 (2016)CrossRefGoogle Scholar
  10. J. Diks, D. Nio, M.A. Linsen, J.A. Rauwerda, W. Wisselink, Suture damage during robot-assisted vascular surgery: Is it an issue? Surg Laparosc Endosc Percutan Tech 17, 524–527 (Dec 2007)CrossRefGoogle Scholar
  11. J. C. Gwilliam, M. Mahvash, B. Vagvolgyi, A. Vacharat, D. D. Yuh, and A. M. Okamura, “Effects of haptic and graphical force feedback on teleoperated palpation,” IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan (May 2009)Google Scholar
  12. Y. Hirano, N. Ishikawa, G. Watanabe, Suture damage after grasping with EndoWrist of the da Vinci surgical system. Minim. Invasive Ther. Allied Technol. 19, 203–206 (Aug 2010)CrossRefGoogle Scholar
  13. M. Kitagawa, D. Dokko, A.M. Okamura, D.D. Yuh, Effect of sensory substitution on suture-manipulation forces for robotic surgical systems. J. Thorac. Cardiovasc. Surg. 129, 151–158 (Jan 2005)CrossRefGoogle Scholar
  14. K.A. LeBlanc, Laparoscopic incisional and ventral hernia repair: Complications-how to avoid and handle. Hernia 8, 323–331 (Dec 2004)CrossRefGoogle Scholar
  15. R.W. Livermore, A.C.M. Chong, D.J. Prohaska, F.W. FCooke, T.L. TJones, Knot security, loop security, and elongation of braided polyblend sutures used for arthroscopic knots. Am J Orthop (Belle Mead NJ) 39, 569–576 (2010)Google Scholar
  16. J. Martell, T. Elmer, N. Gopalsami, Y.S. Park, Visual measurement of suture strain for robotic surgery. Comput Math Methods Med 2011, 879086 (2011)CrossRefGoogle Scholar
  17. D.D. Marucci, A.J. Shakeshaft, J.A. Cartmill, M.R. Cox, S.G. Adams, C.J. Martin, Grasper trauma during laparoscopic cholecystectomy. Aust N Z J Surg 70, 578–581 (Aug 2000)CrossRefGoogle Scholar
  18. J. Melinek, P. Lento, J. Moalli, Postmortem analysis of anastomotic suture line disruption following carotid endarterectomy. J. Forensic Sci. 49, 1077–1081 (Sep 2004)CrossRefGoogle Scholar
  19. Y. Mi, Y. Chan, D. Trau, P. Huang, E. Chen, Micromolding of PDMS scaffolds and microwells for tissue culture and cell patterning: A new method of microfabrication by the self-assembled micropatterns of diblock copolymer micelles. Polymer 47, 5124–5130 (2006)CrossRefGoogle Scholar
  20. P. Puangmali, K. Althoefer, L.D. Seneviratne, D. Murphy, P. Dasgupta, State-of-the-art in force and tactile sensing for minimally invasive surgery. IEEE Sensors J. 8, 371–381 (2008)CrossRefGoogle Scholar
  21. C.E. Reiley, T. Akinbiyi, D. Burschka, D.C. Chang, A.M. Okamura, D.D. Yuh, Effects of visual force feedback on robot-assisted surgical task performance. J. Thorac. Cardiovasc. Surg. 135, 196–202 (Jan 2008)CrossRefGoogle Scholar
  22. H.S. Vitense, J.A. Jacko, V.K. Emery, Multimodal feedback: An assessment of performance and mental workload. Ergonomics 46, 68–87 (Jan 15 2003)CrossRefGoogle Scholar
  23. H. Yousef, M. Boukallel, K. Althoefer, Tactile sensing for dexterous in-hand manipulation in robotics—A review. Sensors Actuators A Phys. 167, 171–187 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yuan Dai
    • 1
    • 2
    return OK on get
  • A. Abiri
    • 2
    • 3
  • J. Pensa
    • 2
    • 3
  • S. Liu
    • 1
  • O. Paydar
    • 2
    • 3
  • H. Sohn
    • 1
  • S. Sun
    • 2
    • 3
  • P. A. Pellionisz
    • 2
    • 3
  • C. Pensa
    • 3
  • E. P. Dutson
    • 2
    • 4
  • W. S. Grundfest
    • 1
    • 2
    • 3
    • 4
  • R. N. Candler
    • 1
    • 2
    • 3
    • 5
    Email author
  1. 1.Department of Electrical and Computer EngineeringUniversity of CaliforniaLos AngelesUSA
  2. 2.Center for Advanced Surgical and Interventional Technology (CASIT)Los AngelesUSA
  3. 3.Department of Biomedical EngineeringUniversity of CaliforniaLos AngelesUSA
  4. 4.Department of SurgeryUniversity of CaliforniaLos AngelesUSA
  5. 5.California NanoSystems InstituteLos AngelesUSA

Personalised recommendations