Surgical Endoscopy

, Volume 27, Issue 5, pp 1721–1729 | Cite as

Face and construct validation of a virtual peg transfer simulator

  • Venkata S. Arikatla
  • Ganesh Sankaranarayanan
  • Woojin Ahn
  • Amine Chellali
  • Suvranu De
  • G. L. Caroline
  • John Hwabejire
  • Marc DeMoya
  • Steven Schwaitzberg
  • Daniel B. Jones
Article

Abstract

Background

The Fundamentals of Laparoscopic Surgery (FLS) trainer box is now established as a standard for evaluating minimally invasive surgical skills. A particularly simple task in this trainer box is the peg transfer task which is aimed at testing the surgeon’s bimanual dexterity, hand–eye coordination, speed, and precision. The Virtual Basic Laparoscopic Skill Trainer (VBLaST©) is a virtual version of the FLS tasks which allows automatic scoring and real-time, subjective quantification of performance without the need of a human proctor. In this article we report validation studies of the VBLaST© peg transfer (VBLaST-PT©) simulator.

Methods

Thirty-five subjects with medical background were divided into two groups: experts (PGY 4–5, fellows, and practicing surgeons) and novices (PGY 1–3). The subjects were asked to perform the peg transfer task on both the FLS trainer box and the VBLaST-PT© simulator; their performance was evaluated based on established metrics of error and time. A new length of trajectory (LOT) metric has also been introduced for offline analysis. A questionnaire was used to rate the realism of the virtual system on a 5-point Likert scale.

Results

Preliminary face validation of the VBLaST-PT© with 34 subjects rated on a 5-point Likert scale questionnaire revealed high scores for all aspects of simulation, with 3.53 being the lowest mean score across all questions. A two-tailed Mann–Whitney test performed on the total scores showed significant (p = 0.001) difference between the groups. A similar test performed on the task time (p = 0.002) and the LOT (p = 0.004) separately showed statistically significant differences between the experts and the novices (p < 0.05). The experts appear to be traversing shorter overall trajectories in less time than the novices.

Conclusion

VBLaST-PT© showed both face and construct validity and has promise as a substitute for the FLS for training peg transfer skills.

Keywords

Virtual reality Surgical training Face validity Construct Laparoscopy Length of trajectory 

References

  1. 1.
    Dawson SL, Kaufman JA (1998) The imperative for medical simulation. Proc IEEE 86(3):479–483CrossRefGoogle Scholar
  2. 2.
    Derossis AM, Fried GM, Abrahamowicz M, Sigman HH, Barkun JS, Meakins JL (1998) Development of a model for training and evaluation of laparoscopic skills. Am J Surg 175(6):482–487PubMedCrossRefGoogle Scholar
  3. 3.
    Dauster B, Steinberg AP, Vassiliou MC, Bergman S, Stanbridge DD, Feldman LS, Fried GM (2005) Validity of the MISTELS simulator for laparoscopy training in urology. J Endourol 19(5):541–545PubMedCrossRefGoogle Scholar
  4. 4.
    McCluney A, Vassiliou M, Kaneva P, Cao J, Stanbridge D, Feldman L, Fried G (2007) FLS simulator performance predicts intraoperative laparoscopic skill. Surg Endosc 21(11):1991–1995PubMedCrossRefGoogle Scholar
  5. 5.
    Liu A, Tendick F, Cleary K, Kaufmann C (2003) A survey of surgical simulation: applications, technology, and education. Presence Teleoper Virtual Environ 12:599–614CrossRefGoogle Scholar
  6. 6.
    Satava RM (2007) Historical review of surgical simulation—a personal perspective. World J Surg 32(2):141–148CrossRefGoogle Scholar
  7. 7.
    Seymour NE, Gallagher AG, Roman SA, O’Brien MK, Bansal VK, Andersen DK, Satava RM (2002) Virtual reality training improves operating room performance. Ann Surg 236(4):458–464PubMedCrossRefGoogle Scholar
  8. 8.
    Jordan JA, Gallagher AG, McGuigan J, McClure N (2001) Virtual reality training leads to faster adaptation to the novel psychomotor restrictions encountered by laparoscopic surgeons. Surg Endosc 15(10):1080–1084PubMedCrossRefGoogle Scholar
  9. 9.
    Fraser SA, Klassen DR, Feldman LS, Ghitulescu GA, Stanbridge D, Fried GM (2003) Evaluating laparoscopic skills: setting the pass/fail score for the MISTELS system. Surg Endosc 17(6):964–967PubMedCrossRefGoogle Scholar
  10. 10.
    Zilles CB, Salisbury JK (1995) A constraint-based god-object method for haptic display. IEEE/RSJ Int Conf Intell Robots Syst 3:146–151Google Scholar
  11. 11.
    Rosser JC, Lynch PJ, Cuddihy L, Gentile DA, Klonsky J, Merrell R (2007) The impact of video games on training surgeons in the twenty first century. Arch Surg 142(2):181–186PubMedCrossRefGoogle Scholar
  12. 12.
    Sutherland LM, Middleton PF, Anthony A, Hamdorf J, Cregan P, Scott D, Maddern GJ (2006) Surgical simulation. Ann Surg 243(3):291–300PubMedCrossRefGoogle Scholar
  13. 13.
    Zhang A, Hünerbein M, Dai Y, Schlag P, Beller S (2008) Construct validity testing of a laparoscopic surgery simulator (Lap Mentor): evaluation of surgical skill with a virtual laparoscopic training simulator. Surg Endosc 22(6):1440–1444PubMedCrossRefGoogle Scholar
  14. 14.
    Epona Medical | LAP-X. Laparoscopy training. Available at http://www.lapx.eu/en/lapx.html. Accessed 13 May 2012
  15. 15.
    Iwata N, Fujiwara M, Kodera Y, Tanaka C, Ohashi N, Nakayama G, Koike M et al (2011) Construct validity of the LapVR virtual-reality surgical simulator. Surg Endosc 25(2):423–428PubMedCrossRefGoogle Scholar
  16. 16.
    Mansour S, Din N, Ratnasingham K, Irukulla S, Vasilikostas G, Reddy M, Wan A (2012) Objective assessment of the core laparoscopic skills course. Minim Invasive Surg 2012:379625PubMedGoogle Scholar
  17. 17.
    Pitzul KB, Grantcharov TP, Okrainec A (2012) Validation of three virtual reality Fundamentals of Laparoscopic Surgery (FLS) modules. Stud Health Technol Inf 173:349–355Google Scholar
  18. 18.
    Ritter E, Kindelan T, Michael C, Pimentel E, Bowyer M (2007) Concurrent validity of augmented reality metrics applied to the fundamentals of laparoscopic surgery (FLS). Surg Endosc 21(8):1441–1445PubMedCrossRefGoogle Scholar
  19. 19.
    Larsen CR, Grantcharov T, Aggarwal R, Tully A, Sørensen JL, Dalsgaard T, Ottesen B (2006) Objective assessment of gynecologic laparoscopic skills using the LapSimGyn virtual reality simulator. Surg Endosc 20(9):1460–1466PubMedCrossRefGoogle Scholar
  20. 20.
    Chmarra MK, Jansen FW, Grimbergen CA, Dankelman J (2008) Retracting and seeking movements during laparoscopic goal-oriented movements. Is the shortest path length optimal? Surg Endosc 22(4):943–949PubMedCrossRefGoogle Scholar
  21. 21.
    Verner L, Oleynikov D, Holtmann S, Haider H, Zhukov L (2003) Measurements of the level of surgical expertise using flight path analysis from da Vinci robotic surgical system. Stud Health Technol Inf 94:373–378Google Scholar
  22. 22.
    Rosen J, MacFarlane M, Richards C, Hannaford B, Sinanan M (1999) Surgeon-tool force/torque signatures—evaluation of surgical skills in minimally invasive surgery. Stud Health Technol Inf 62:290–296Google Scholar
  23. 23.
    Hwang H, Lim J, Kinnaird C, Nagy A, Panton O, Hodgson A, Qayumi K (2006) Correlating motor performance with surgical error in laparoscopic cholecystectomy. Surg Endosc 20(4):651–655PubMedCrossRefGoogle Scholar
  24. 24.
    Kowalewski TM, Rosen J, Chang L, Sinanan MN, Hannaford B (2004) Optimization of a vector quantization codebook for objective evaluation of surgical skill. Stud Health Technol Inf 98:174–179Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Venkata S. Arikatla
    • 1
  • Ganesh Sankaranarayanan
    • 1
  • Woojin Ahn
    • 1
  • Amine Chellali
    • 2
  • Suvranu De
    • 1
    • 6
  • G. L. Caroline
    • 5
  • John Hwabejire
    • 3
  • Marc DeMoya
    • 3
  • Steven Schwaitzberg
    • 2
  • Daniel B. Jones
    • 4
  1. 1.Center for Modeling, Simulation and Imaging in MedicineRensselaer Polytechnic InstituteTroyUSA
  2. 2.Department of Surgery, Cambridge Health AllianceHarvard Medical SchoolBostonUSA
  3. 3.Division of Trauma, Massachusetts General HospitalHarvard Medical SchoolBostonUSA
  4. 4.Department of Surgery, Beth Israel Deaconess Medical CenterHarvard Medical SchoolBostonUSA
  5. 5.Department of Biomedical, Industrial and Human Factors EngineeringWright State UniversityFairbornUSA
  6. 6.Department of Mechanical, Aerospace and Nuclear EngineeringRensselaer Polytechnic InstituteTroyUSA

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