Surgical Endoscopy

, Volume 31, Issue 12, pp 5012–5023 | Cite as

Virtual reality-based assessment of basic laparoscopic skills using the Leap Motion controller

  • Vasileios Lahanas
  • Constantinos LoukasEmail author
  • Konstantinos Georgiou
  • Hani Lababidi
  • Dania Al-Jaroudi



The majority of the current surgical simulators employ specialized sensory equipment for instrument tracking. The Leap Motion controller is a new device able to track linear objects with sub-millimeter accuracy. The aim of this study was to investigate the potential of a virtual reality (VR) simulator for assessment of basic laparoscopic skills, based on the low-cost Leap Motion controller.


A simple interface was constructed to simulate the insertion point of the instruments into the abdominal cavity. The controller provided information about the position and orientation of the instruments. Custom tools were constructed to simulate the laparoscopic setup. Three basic VR tasks were developed: camera navigation (CN), instrument navigation (IN), and bimanual operation (BO). The experiments were carried out in two simulation centers: MPLSC (Athens, Greece) and CRESENT (Riyadh, Kingdom of Saudi Arabia). Two groups of surgeons (28 experts and 21 novices) participated in the study by performing the VR tasks. Skills assessment metrics included time, pathlength, and two task-specific errors. The face validity of the training scenarios was also investigated via a questionnaire completed by the participants.


Expert surgeons significantly outperformed novices in all assessment metrics for IN and BO (p < 0.05). For CN, a significant difference was found in one error metric (p < 0.05). The greatest difference between the performances of the two groups occurred for BO. Qualitative analysis of the instrument trajectory revealed that experts performed more delicate movements compared to novices. Subjects’ ratings on the feedback questionnaire highlighted the training value of the system.


This study provides evidence regarding the potential use of the Leap Motion controller for assessment of basic laparoscopic skills. The proposed system allowed the evaluation of dexterity of the hand movements. Future work will involve comparison studies with validated simulators and development of advanced training scenarios on current Leap Motion controller.


Leap Motion controller Instrument tracking Surgical simulation Surgical training Laparoscopic skills 



We would like to thank Ms. Ouhoud Kaddour who helped in organizing the data collection and contributed in the necessary coordination of the study, and Mr. Jalal Froukh for his assistance and the setup of the equipment during the study period.


Funding was provided by King Fahad Medical City-Riyadh Saudi Arabia (Intramural fund number 015-006).

Compliance with ethical standard


Dr. Al-Jaroudi, Dr. Loukas, and Dr. Lahanas report grants from King Fahad Medical City (Riyadh, KSA) during the conduct of the study. Dr. Lababidi and Dr. Georgiou have no conflicts of interest or financial ties to disclose.

Supplementary material

Supplementary material 1 (WMV 16576 KB)


  1. 1.
    Drews FA, Bakdash JZ (2013) Simulation training in health care. Rev Hum Factors Ergon 8:191–234. doi: 10.1177/1557234X13492977 CrossRefGoogle Scholar
  2. 2.
    Dawe SR, Windsor JA, Broeders JAJL, Cregan PC, Hewett PJ, Maddern GJ (2014) A systematic review of surgical skills transfer after simulation-based training: laparoscopic cholecystectomy and endoscopy. Ann Surg 259:236–248. doi: 10.1097/SLA.0000000000000245 CrossRefPubMedGoogle Scholar
  3. 3.
    De Win G, Van Bruwaene S, Aggarwal R, Crea N, Zhang Z, De Ridder D, Miserez M (2013) Laparoscopy training in surgical education: the utility of incorporating a structured preclinical laparoscopy course into the traditional apprenticeship method. J Surg Educ 70:596–605. doi: 10.1016/j.jsurg.2013.04.001 CrossRefPubMedGoogle Scholar
  4. 4.
    Bashankaev B, Baido S, Wexner SD (2011) Review of available methods of simulation training to facilitate surgical education. Surg Endosc 25:28–35. doi: 10.1007/s00464-010-1123-x CrossRefPubMedGoogle Scholar
  5. 5.
    Palter VN, Grantcharov TP (2010) Simulation in surgical education. Can Med Assoc J 182:1191–1196. doi: 10.1503/cmaj.091743 CrossRefGoogle Scholar
  6. 6.
    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 53:399–413. doi: 10.1109/TBME.2005.869771 CrossRefPubMedGoogle Scholar
  7. 7.
    Dosis A, Aggarwal R, Bello F, Moorthy K, Munz Y, Gillies D, Darzi A (2005) Synchronized video and motion analysis for the assessment of procedures in the operating theater. Arch Surg 140:293–299. doi: 10.1001/archsurg.140.3.293 CrossRefPubMedGoogle Scholar
  8. 8.
    Lahanas V, Loukas C, Smailis N, Georgiou E (2015) A novel augmented reality simulator for skills assessment in minimal invasive surgery. Surg Endosc 29:2224–2234. doi: 10.1007/s00464-014-3930-y CrossRefPubMedGoogle Scholar
  9. 9.
    Katić D, Wekerle A-L, Görtler J, Spengler P, Bodenstedt S, Röhl S, Suwelack S, Kenngott HG, Wagner M, Müller-Stich BP, Dillmann R, Speidel S (2013) Context-aware augmented reality in laparoscopic surgery. Comput Med Imaging Graph 37:174–182. doi: 10.1016/j.compmedimag.2013.03.003 CrossRefPubMedGoogle Scholar
  10. 10.
    Sierra R, Dimaio SP, Wada J, Hata N, Székely G, Kikinis R, Jolesz F (2007) Patient specific simulation and navigation of ventriculoscopic interventions. Stud Health Technol Inform 125:433–435PubMedGoogle Scholar
  11. 11.
    Speidel S, Sudra G, Senemaud J, Drentschew M, Müller-Stich BP, Gutt C, Dillmann R (2008) Recognition of risk situations based on endoscopic instrument tracking and knowledge based situation modeling. In: Miga MI, Cleary KR (eds) SPIE, Med. Imaging. p 69180X–69180X–8Google Scholar
  12. 12.
    Kato H, Billinghurst M (1999) Marker tracking and HMD calibration for a video-based augmented reality conferencing system. In: Proc. 2nd IEEE ACM Int. Work. Augment. Real. IEEE Comput. Soc, pp 85–94Google Scholar
  13. 13.
    Nicolau SA, Vemuri A, Wu HS, Huang MH, Ho Y, Charnoz A, Hostettler A, Forest C, Soler L, Marescaux J (2011) A cost effective simulator for education of ultrasound image interpretation and probe manipulation. Stud Health Technol Inform 163:403–407PubMedGoogle Scholar
  14. 14.
    Lahanas V, Loukas C, Nikiteas N, Dimitroulis D, Georgiou E (2011) Psychomotor skills assessment in laparoscopic surgery using augmented reality scenarios. In: 17th Int. Conf. Digit. Signal Process. IEEE, pp 1–6Google Scholar
  15. 15.
    Cano AM, Lamata P, Gayá F, Gómez EJ (2006) New methods for video-based tracking of laparoscopic tools. Lect Notes Comput Sci 4072:142–149CrossRefGoogle Scholar
  16. 16.
    Loukas C, Lahanas V, Georgiou E (2013) An integrated approach to endoscopic instrument tracking for augmented reality applications in surgical simulation training. Int J Med Robot Comput Assist Surg 9:e34–e51. doi: 10.1002/rcs.1485 CrossRefGoogle Scholar
  17. 17.
    Shin S, Kim Y, Kwak H, Lee D, Park S (2011) 3D tracking of surgical instruments using a single camera for laparoscopic surgery simulation. Stud Health Technol Inform 163:581–587PubMedGoogle Scholar
  18. 18.
    Voros S, Long J-A, Cinquin P (2007) Automatic detection of instruments in laparoscopic images: a first step towards high-level command of robotic endoscopic holders. Int J Rob Res 26:1173–1190. doi: 10.1177/0278364907083395 CrossRefGoogle Scholar
  19. 19.
    Chmarra MK, Grimbergen CA, Dankelman J (2007) Systems for tracking minimally invasive surgical instruments. Minim Invasive Ther Allied Technol 16:328–340. doi: 10.1080/13645700701702135 CrossRefPubMedGoogle Scholar
  20. 20.
    Weichert F, Bachmann D, Rudak B, Fisseler D (2013) Analysis of the accuracy and robustness of the leap motion controller. Sensors 13:6380–6393. doi: 10.3390/s130506380 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Sánchez-González P, Cano AM, Oropesa I, Sánchez-Margallo FM, Del Pozo F, Lamata P, Gómez EJ (2011) Laparoscopic video analysis for training and image-guided surgery. Minim Invasive Ther Allied Technol 20:311–320. doi: 10.3109/13645706.2010.541921 CrossRefPubMedGoogle Scholar
  22. 22.
    Holz D (2014) Systems and methods for capturing motion in three-dimensional space.Google Scholar
  23. 23.
    Halvorsen FH, Elle OJ, Fosse E (2005) Simulators in surgery. Minim Invasive Ther Allied Technol 14:214–223. doi: 10.1080/13645700500243869 CrossRefPubMedGoogle Scholar
  24. 24.
    Maithel S, Sierra R, Korndorffer J, Neumann P, Dawson S, Callery M, Jones D, Scott D (2006) Construct and face validity of MIST-VR, Endotower, and CELTS: are we ready for skills assessment using simulators? Surg Endosc 20:104–112. doi: 10.1007/s00464-005-0054-4 CrossRefPubMedGoogle Scholar
  25. 25.
    Woodrum DT, Andreatta PB, Yellamanchilli RK, Feryus L, Gauger PG, Minter RM (2006) Construct validity of the LapSim laparoscopic surgical simulator. Am J Surg 191:28–32. doi: 10.1016/j.amjsurg.2005.10.018 CrossRefPubMedGoogle Scholar
  26. 26.
    Panait L, Akkary E, Bell RL, Roberts KE, Dudrick SJ, Duffy AJ (2009) The role of haptic feedback in laparoscopic simulation training. J Surg Res 156:312–316. doi: 10.1016/j.jss.2009.04.018 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Medical Physics Lab-Simulation CenterSchool of Medicine, National and Kapodistrian University of AthensAthensGreece
  2. 2.Center for Research, Education & Simulation Enhanced TrainingKing Fahad Medical CityRiyadhSaudi Arabia

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