title: Characterizing the learning curve of the VBLaST-PT(©) (Virtual Basic Laparoscopic Skill Trainer).
abstract: BACKGROUND
  Mastering laparoscopic surgical skills requires considerable time and effort. The Virtual Basic Laparoscopic Skill Trainer (VBLaST-PT(©)) is being developed as a computerized version of the peg transfer task of the Fundamentals of Laparoscopic Surgery (FLS) system using virtual reality technology. We assessed the learning curve of trainees on the VBLaST-PT(©) using the cumulative summation (CUSUM) method and compared them with those on the FLS to establish convergent validity for the VBLaST-PT(©).
  
  
  METHODS
  Eighteen medical students from were assigned randomly to one of three groups: control, VBLaST-training, and FLS-training. The VBLaST and the FLS groups performed a total of 150 trials of the peg-transfer task over a 3-week period, 5 days a week. Their CUSUM scores were computed based on predefined performance criteria (junior, intermediate, and senior levels).
  
  
  RESULTS
  Of the six subjects in the VBLaST-training group, five achieved at least the "junior" level, three achieved the "intermediate" level, and one achieved the "senior" level of performance criterion by the end of the 150 trials. In comparison, for the FLS group, three students achieved the "senior" criterion and all six students achieved the "intermediate" and "junior" criteria by the 150th trials. Both the VBLaST-PT(©) and the FLS systems showed significant skill improvement and retention, albeit with system specificity as measured by transfer of learning in the retention test: The VBLaST-trained group performed better on the VBLaST-PT(©) than on FLS (p = 0.003), whereas the FLS-trained group performed better on the FLS than on VBLaST-PT(©) (p = 0.002).
  
  
  CONCLUSIONS
  We characterized the learning curve for a virtual peg transfer task on the VBLaST-PT(©) and compared it with the FLS using CUSUM analysis. Subjects in both training groups showed significant improvement in skill performance, but the transfer of training between systems was not significant.
authors.affiliation: Department of Mechanical Engineering, Tufts University, Medford, MA, USA.
authors.forename: Likun
authors.initials: L
authors.lastname: Zhang
authors.forename: Ganesh
authors.initials: G
authors.lastname: Sankaranarayanan
authors.forename: Venkata Sreekanth
authors.initials: VS
authors.lastname: Arikatla
authors.forename: Woojin
authors.initials: W
authors.lastname: Ahn
authors.forename: Cristol
authors.initials: C
authors.lastname: Grosdemouge
authors.forename: Jesse M
authors.initials: JM
authors.lastname: Rideout
authors.forename: Scott K
authors.initials: SK
authors.lastname: Epstein
authors.forename: Suvranu
authors.initials: S
authors.lastname: De
authors.forename: Steven D
authors.initials: SD
authors.lastname: Schwaitzberg
authors.forename: Daniel B
authors.initials: DB
authors.lastname: Jones
authors.forename: Caroline G L
authors.initials: CG
authors.lastname: Cao
mesh_terms: D000328:Adult; D003198:Computer Simulation; D004521:Educational Measurement; D006801:Humans; D010535:Laparoscopy / Q000193:education*; D059032:Learning Curve; D061646:Operative Time; D011897:Random Allocation; D013337:Students, Medical; D013647:Task Performance and Analysis; D014110:Touch; D014584:User-Computer Interface; D014741:Video Recording