Development and face validation of a virtual camera navigation task trainer

Surg Endosc. 2019 Jun;33(6):1927-1937. doi: 10.1007/s00464-018-6476-6. Epub 2018 Oct 15.

Abstract

Background: The fundamentals of laparoscopic surgery (FLS) trainer box, which is now established as a standard for evaluating minimally invasive surgical skills, consists of five tasks: peg transfer, pattern cutting, ligation, intra- and extracorporeal suturing. Virtual simulators of these tasks have been developed and validated as part of the Virtual Basic Laparoscopic Skill Trainer (VBLaST) (Arikatla et al. in Int J Med Robot Comput Assist Surg 10:344-355, 2014; Zhang et al. in Surg Endosc 27(10):3603-3615, 2013; Sankaranarayanan et al. in J Laparoendosc Adv Surg Tech 20(2):153-157, 2010; Qi et al. J Biomed Inform 75:48-62, 2017). The virtual task trainers have many advantages including automatic real-time objective scoring, reduced costs, and eliminating human proctors. In this paper, we extend VBLaST by adding two camera navigation system tasks: (a) pattern matching and (b) path tracing.

Methods: A comprehensive camera navigation simulator with two virtual tasks, simplified and cheaper hardware interface (compared to the prior version of VBLaST), graphical user interface, and automated metrics has been designed and developed. Face validity of the system is tested with medical students and residents from the University at Buffalo's medical school.

Results: The subjects rated the simulator highly in all aspects including its usefulness in training to center the target and to teach sizing skills. The quality and usefulness of the force feedback scored the lowest at 2.62.

Keywords: Camera navigation; Face validity; Laparoscopy; Surgical training; Virtual reality.

Publication types

  • Research Support, N.I.H., Extramural
  • Validation Study

MeSH terms

  • Adult
  • Clinical Competence
  • Computer Simulation*
  • Female
  • Humans
  • Laparoscopy / education*
  • Male
  • Simulation Training*
  • User-Computer Interface
  • Young Adult