Skip to main content
Log in

Da Vinci© Skills Simulator™: is an early selection of talented console surgeons possible?

  • Review Article
  • Published:
Journal of Robotic Surgery Aims and scope Submit manuscript

Abstract

To investigate whether the learning curve of robotic surgery simulator training depends on the probands’ characteristics, such as age and prior experience, we conducted a study of six distinct proband groups, using the da Vinci Skills Simulator: experienced urological robotic surgeons, surgeons with experience as da Vinci tableside assistants, urological surgeons with laparoscopic experience, urological surgeons without laparoscopic experience, and complete novices aged 25 and younger and 40 and older. The results showed that all experienced robotic surgeons reached expert level (>90 %, as defined previously in the literature) within the first three repetitions and remained on a high level of performance. All other groups performed worse. Tableside assistants, laparoscopically experienced surgeons, and younger novices showed a better performance in all exercises than surgeons without laparoscopic experience and older novices. A linear mixed-effects model analysis demonstrated no significant difference in learning curves between proband groups in all exercises except the RW1 exercise for the younger proband group. In summary, we found that performance in robotic surgery, measured by performance scores in three virtual simulator modules using the EndoWrist techniques, was dependent on age and prior experience with robotic and laparoscopic surgery. However, and most importantly, the learning curve was not significantly affected by these factors. This suggests that the da Vinci Skills Simulator™ is a useful practice tool for everyone learning or performing robotic surgery, and that early selection of talented surgeons is neither possible nor necessary.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Yates DR, Vaessen C, Roupret M (2011) From Leonardo to da Vinci: the history of robot-assisted surgery in urology. BJU Int 108:1708–1713. doi:10.1111/j.1464-410X.2011.10576.x (discussion 1714)

    Article  PubMed  Google Scholar 

  2. Rassweiler J, Hruza M, Teber D, Su L-M (2006) Laparoscopic and robotic assisted radical prostatectomy—critical analysis of the results. Eur Urol 49:612–624. doi:10.1016/j.eururo.2005.12.054

    Article  PubMed  Google Scholar 

  3. Ou YC, Yang CR, Wang J et al (2010) Robotic-assisted laparoscopic radical prostatectomy: learning curve of first 100 cases. Int J Urol Off J Jpn Urol Assoc 17:635–640. doi:10.1111/j.1442-2042.2010.02546.x

    Google Scholar 

  4. Weinberg L, Rao S, Escobar PF (2011) Robotic surgery in gynecology: an updated systematic review. Obstet Gynecol Int 2011:852061. doi:10.1155/2011/852061

    Article  PubMed  PubMed Central  Google Scholar 

  5. Salö M, Sjöberg Altemani T, Anderberg M (2016) Pyeloplasty in children: perioperative results and long-term outcomes of robotic-assisted laparoscopic surgery compared to open surgery. Pediatr Surg Int. doi:10.1007/s00383-016-3869-2

    Google Scholar 

  6. Meyer M, Gharagozloo F, Tempesta B et al (2012) The learning curve of robotic lobectomy. Int J Med Robot Comput Assist Surg MRCAS 8:448–452. doi:10.1002/rcs.1455

    Article  Google Scholar 

  7. Orvieto MA, Marchetti P, Castillo OA et al (2011) Robotic technologies in surgical oncology training and practice. Surg Oncol 20:203–209. doi:10.1016/j.suronc.2010.08.005

    Article  PubMed  Google Scholar 

  8. Hu D, Gong Y, Hannaford B, Seibel EJ (2015) Path planning for semi-automated simulated robotic neurosurgery. Proc IEEERSJ Int Conf Intell Robots Syst 2015:2639–2645. doi:10.1109/IROS.2015.7353737

    Google Scholar 

  9. Bourcier T, Chammas J, Becmeur P-H et al (2015) Robotically assisted pterygium surgery: first human case. Cornea 34:1329–1330. doi:10.1097/ICO.0000000000000561

    Article  PubMed  Google Scholar 

  10. Health Quality Ontario (2010) Robotic-assisted minimally invasive surgery for gynecologic and urologic oncology: an evidence-based analysis. Ont Health Technol Assess Ser 10:1–118

    Google Scholar 

  11. Amodeo A, Linares Quevedo A, Joseph JV et al (2009) Robotic laparoscopic surgery: cost and training. Minerva Urol E Nefrol Ital J Urol Nephrol 61:121–128

    CAS  Google Scholar 

  12. Moore LJ, Wilson MR, McGrath JS et al (2015) Surgeons’ display reduced mental effort and workload while performing robotically assisted surgical tasks, when compared to conventional laparoscopy. Surg Endosc 29:2553–2560. doi:10.1007/s00464-014-3967-y

    Article  PubMed  Google Scholar 

  13. Kho RM (2011) Comparison of robotic-assisted laparoscopy versus conventional laparoscopy on skill acquisition and performance. Clin Obstet Gynecol 54:376–381. doi:10.1097/GRF.0b013e31822b46f6

    Article  PubMed  Google Scholar 

  14. Lavery HJ, Small AC, Samadi DB, Palese MA (2011) Transition from laparoscopic to robotic partial nephrectomy: the learning curve for an experienced laparoscopic surgeon. JSLS J Soc Laparoendosc Surg Soc Laparoendosc Surg 15:291–297. doi:10.4293/108680811X13071180407357

    Article  Google Scholar 

  15. Patel VR, Tully AS, Holmes R, Lindsay J (2005) Robotic radical prostatectomy in the community setting—the learning curve and beyond: initial 200 cases. J Urol 174:269–272. doi:10.1097/01.ju.0000162082.12962.40

    Article  PubMed  Google Scholar 

  16. Diana M, Marescaux J (2015) Robotic surgery. Br J Surg 102:e15–e28. doi:10.1002/bjs.9711

    Article  CAS  PubMed  Google Scholar 

  17. Hung AJ, Patil MB, Zehnder P et al (2012) Concurrent and predictive validation of a novel robotic surgery simulator: a prospective, randomized study. J Urol 187:630–637. doi:10.1016/j.juro.2011.09.154

    Article  PubMed  Google Scholar 

  18. Seixas-Mikelus SA, Kesavadas T, Srimathveeravalli G et al (2010) Face validation of a novel robotic surgical simulator. Urology 76:357–360. doi:10.1016/j.urology.2009.11.069

    Article  PubMed  Google Scholar 

  19. Seixas-Mikelus SA, Stegemann AP, Kesavadas T et al (2011) Content validation of a novel robotic surgical simulator. BJU Int 107:1130–1135. doi:10.1111/j.1464-410X.2010.09694.x

    Article  PubMed  Google Scholar 

  20. Kesavadas T, Stegemann A, Sathyaseelan G et al (2011) Validation of robotic surgery simulator (RoSS). Stud Health Technol Inform 163:274–276

    PubMed  Google Scholar 

  21. Lerner MA, Ayalew M, Peine WJ, Sundaram CP (2010) Does training on a virtual reality robotic simulator improve performance on the da Vinci surgical system? J Endourol Endourol Soc 24:467–472. doi:10.1089/end.2009.0190

    Article  Google Scholar 

  22. Ramos P, Montez J, Tripp A et al (2014) Face, content, construct and concurrent validity of dry laboratory exercises for robotic training using a global assessment tool. BJU Int 113:836–842. doi:10.1111/bju.12559

    Article  PubMed  Google Scholar 

  23. Kenney PA, Wszolek MF, Gould JJ et al (2009) Face, content, and construct validity of dV-trainer, a novel virtual reality simulator for robotic surgery. Urology 73:1288–1292. doi:10.1016/j.urology.2008.12.044

    Article  PubMed  Google Scholar 

  24. Perrenot C, Perez M, Tran N et al (2012) The virtual reality simulator dV-Trainer(®) is a valid assessment tool for robotic surgical skills. Surg Endosc 26:2587–2593. doi:10.1007/s00464-012-2237-0

    Article  PubMed  Google Scholar 

  25. Korets R, Mues AC, Graversen JA et al (2011) Validating the use of the Mimic dV-trainer for robotic surgery skill acquisition among urology residents. Urology 78:1326–1330. doi:10.1016/j.urology.2011.07.1426

    Article  PubMed  Google Scholar 

  26. Lee JY, Mucksavage P, Kerbl DC et al (2012) Validation study of a virtual reality robotic simulator—role as an assessment tool? J Urol 187:998–1002. doi:10.1016/j.juro.2011.10.160

    Article  PubMed  Google Scholar 

  27. Lendvay TS, Casale P, Sweet R, Peters C (2008) VR robotic surgery: randomized blinded study of the dV-Trainer robotic simulator. Stud Health Technol Inform 132:242–244

    PubMed  Google Scholar 

  28. Walliczek U, Förtsch A, Dworschak P et al (2015) Effect of training frequency on the learning curve on the da Vinci Skills Simulator. Head Neck. doi:10.1002/hed.24312

    PubMed  Google Scholar 

  29. Hagen ME, Wagner OJ, Inan I, Morel P (2009) Impact of IQ, computer-gaming skills, general dexterity, and laparoscopic experience on performance with the da Vinci surgical system. Int J Med Robot Comput Assist Surg MRCAS 5:327–331. doi:10.1002/rcs.264

    Article  Google Scholar 

  30. Yoo BE, Kim J, Cho JS et al (2015) Impact of laparoscopic experience on virtual robotic simulator dexterity. J Minimal Access Surg 11:68–71. doi:10.4103/0972-9941.147696

    Article  Google Scholar 

  31. Kim HJ, Choi G-S, Park JS, Park SY (2014) Comparison of surgical skills in laparoscopic and robotic tasks between experienced surgeons and novices in laparoscopic surgery: an experimental study. Ann Coloproctol 30:71–76. doi:10.3393/ac.2014.30.2.71

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mark Meier.

Ethics declarations

Conflict of interest

Meier M, Horton K, and John H declare that they have no conflict of interest.

Ethics

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed written consent was obtained from all individual participants included in the study. The participants were informed that the researchers were not affiliated with the manufacturer of the da Vinci Surgical System and the da Vinci Skills Simulator™ and that all data were to be analyzed anonymously.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 253 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meier, M., Horton, K. & John, H. Da Vinci© Skills Simulator™: is an early selection of talented console surgeons possible?. J Robotic Surg 10, 289–296 (2016). https://doi.org/10.1007/s11701-016-0616-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11701-016-0616-6

Keywords

Navigation