Advertisement

Surgical Endoscopy

, Volume 28, Issue 4, pp 1360–1370 | Cite as

Automatic supervision of gestures to guide novice surgeons during training

  • C. MonserratEmail author
  • A. Lucas
  • J. Hernández-Orallo
  • M. José Rupérez
New Technology

Abstract

Background

Virtual surgery simulators enable surgeons to learn by themselves, shortening their learning curves. Virtual simulators offer an objective evaluation of the surgeon’s skills at the end of each training session. The considered evaluation parameters are based on the analysis of the surgeon’s gestures performed throughout the training session. Currently, this information is usually known by surgeons only at the end of the training session, but very limited during the training performance. In this paper, we present a novel method for automatic and interactive evaluation of the surgeon’s skills that is able to supervise inexperienced surgeons during their training session with surgical simulators.

Methods

The method is based on the assumption that the sequence of gestures carried out by an expert surgeon in the simulator can be translated into a sequence (a character string) that should be reproduced by a novice surgeon during a training session. In this work, a string-matching algorithm has been modified to calculate the alignment and distance between the sequences of both expert and novice during the training performance.

Results

The results have shown that it is possible to distinguish between different skill levels at all times during the surgical training session.

Conclusions

The main contribution of this paper is a method where the difference between an expert’s sequence of gestures and a novice’s ongoing sequence is used to guide inexperienced surgeons. This is possible by indicating to novices the gesture corrections to be applied during surgical training as continuous expert supervision would do.

Keywords

Imaging & VR Technical surgical Technical human/robotic Technical computing Technical training Endoscopy 

Notes

Acknowledgments

The authors thank Dr. Francisco Dolz, Director of the Area of Clinical Simulation and Instructor of Clinical Simulation of the Hospital Universitari i Politècnic La Fe of Valencia (Spain), for his revision of the paper and his advice and suggestions. We also thank the anonymous reviewers for their comments, which have helped to improve the paper.

Disclosures

Dr. Carlos Monserrat, Alejandro Lucas, Dr. José Hernández, Dr. M. José Rupérez, and Dr. Mariano Alcañiz have no conflicts of interest or financial ties to disclose.

References

  1. 1.
    Ericsson KA (ed) (2009) Development of professional expertise: toward measurement of expert performance and design of optimal learning environments. Cambridge University Press, New YorkGoogle Scholar
  2. 2.
    McGaghie WC (2008) Research opportunities in simulation-based medical education using deliberate practice. Acad Emerg Med 15:995–1001PubMedCrossRefGoogle Scholar
  3. 3.
    Ericsson KA (2008) Deliberate practice and acquisition of expert performance: a general overview. Acad Emerg Med 15:988–994PubMedCrossRefGoogle Scholar
  4. 4.
    Issenberg SB, McGaghie WC, Petrusa ER et al (2005) Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach 27:10–28PubMedCrossRefGoogle Scholar
  5. 5.
    Porte MC, Xeoulis G, Reznick RK, Dubrowski A (2007) Verbal feedback from an expert is more effective than self-accessed feedback about motion efficiency in learning new surgical skills. Am J Surg 193:105–110. doi: 10.1016/j.amjsurg.2006.03.016 PubMedCrossRefGoogle Scholar
  6. 6.
    Hall PAV, Dowling GR (1980) Approximate string matching. ACM computing surveys (CSUR) 18(2):381–402. doi: 10.1145/356827.356830 CrossRefGoogle Scholar
  7. 7.
    Stylopoulos N, Cotin S, Maithel SK et al (2004) Computer-enhanced laparoscopic training system (CELTS): bridging the gap. Surg Endosc 18(5):782–789. doi: 10.3233/978-1-60750-938-7-336 PubMedGoogle Scholar
  8. 8.
    Solis J, Oshima N, Ishii H, Matsuoka N et al (2009) Quantitative assessment of the surgical training methods with the suture/ligature training system WKS-2RII. In: IEEE international conference on robotics and automation, 2009 (ICRA ‘09), Kobe, pp 4219–4224. doi: 10.1109/ROBOT.2009.5152314
  9. 9.
    Lin Z et al (2010) Objective evaluation of laparoscopic surgical skills using Waseda bioinstrumentation system WB-3. In: IEEE international conference on robotics and biomimetics (ROBIO), Tianjin, pp 247–252. doi: 10.1109/ROBIO.2010.5723335
  10. 10.
    Chmarra MK, Klein S, Winter JCF, Jansen FW, Dankelman J (2010) Objective classification of residents based on their psychomotor laparoscopic skills. Surg Endosc 24(5):1031–1039. doi: 10.1007/s00464-009-0721-y PubMedCrossRefPubMedCentralGoogle Scholar
  11. 11.
    Lin HC, Shafran I, Yuh D, Hager GD (2006) Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions. Comput Aided Surg 11(5):220–230. doi: 10.3109/10929080600989189 PubMedCrossRefGoogle Scholar
  12. 12.
    Rosen J, Brown JD, Chang L, Sinanan MN, Hannaford B (2006) Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model. IEEE Trans Biomed Eng 53(3):399–413. doi: 10.1109/TBME.2005.869771 PubMedCrossRefGoogle Scholar
  13. 13.
    Lahanas V, Loukas C, Nikiteas N, Dimitroulis D, Georgiou E (2011) Psychomotor skills assessment in laparoscopic surgery using augmented reality scenarios. In: 17th international conference on digital signal processing (DSP), Corfu. doi: 10.1109/ICDSP.2011.6004893
  14. 14.
    Leong JJ et al (2006) HMM assessment of quality of movement trajectory in laparoscopic surgery. In: International conference on medical image computing and computer-assisted intervention (MICCAI’06), pp 752–759. doi: 10.3109/10929080701730979
  15. 15.
    Megali G, Sinigaglia S, Tonet O, Dario P (2006) Modelling and evaluation of surgical performance using Hidden Markov models. IEEE Trans Biomed Eng 53(10):1911–1919. doi: 10.1109/TBME.2006.881784 PubMedCrossRefGoogle Scholar
  16. 16.
    Huang J, Payandeh S, Doris P, Hajshirmohammadi I (2005) Fuzzy classification: towards evaluating performance on a surgical simulator. Stud Health Technol Inform 111:194–200PubMedGoogle Scholar
  17. 17.
    Hajshirmohammadi I, Payandeh S (2007) Fuzzy set theory for performance evaluation in a surgical simulator. Presence 16(6):603–622. doi: 10.1162/pres.16.6.603 CrossRefGoogle Scholar
  18. 18.
    Ukkonen E (1985) Algorithms for approximate string matching. Inf Control 64(1–3):100–118. doi: 10.1016/S0019-9958(85)80046-2 CrossRefGoogle Scholar
  19. 19.
    Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv 33(1):31–88. doi: 10.1145/375360.375365 CrossRefGoogle Scholar
  20. 20.
    Damerau FJ (1964) A technique for computer detection and correction of spelling errors. Commun ACM 7(3):171–176. doi: 10.1145/363958.363994 CrossRefGoogle Scholar
  21. 21.
    Bergroth L, Hakonen H, Raita T (2000) A survey of longest common subsequence algorithms. In: Proceedings of the seventh international symposium on string processing information retrieval (SPIRE’00), A Coruña, p 39. doi: 10.1109/SPIRE.2000.878178
  22. 22.
    Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334. doi: 10.1109/34.888718 CrossRefGoogle Scholar
  23. 23.
    Simbionix™, Lap Mentor™. simbionix.com. http://simbionix.com/simulators/lap-mentor/library-of-modules/basic-skills/. Accessed 31 Jan 2013
  24. 24.
    Wagner RA, Fischer MJ (1974) Algorithms for approximate string matching. J ACM 21(1):168–173. doi: 10.1016/S0019-9958(85)80046-2 CrossRefGoogle Scholar
  25. 25.
    Hirschberg DS (1975) A linear space algorithm for computing maximal common subsequences. Commun ACM 18(6):341–343. doi: 10.1145/360825.360861 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • C. Monserrat
    • 1
    Email author
  • A. Lucas
    • 1
  • J. Hernández-Orallo
    • 2
  • M. José Rupérez
    • 1
  1. 1.LabHuman, Ciudad Politécnica de la InnovaciónUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Departamento de Sistemas Informáticos y ComputaciónUniversitat Politècnica de ValènciaValenciaSpain

Personalised recommendations