title: A study on the learning curve of the robotic virtual reality simulator.
abstract: OBJECTIVE
  A robotic virtual reality simulator (dV-Trainer™, Mimic Technologies) has been accepted as an effective training tool for the da Vinci(®) Surgical System (Intuitive Surgical, Inc.) in previous reports. However, there are no data available so far on how much time is required for an individual using the simulator to become proficient. We investigated how long and how many performances it takes to gain proficiency with the robotic virtual reality simulator through the learning curve.
  
  
  METHODS
  The novice group included 20 medical students who had no previous experience. The "Tube 2" task, a program released for the dV-Trainer that imitates a vesicourethral anastomosis, was repeated more than 80 times to obtain the plateau of the learning curve. The learning curve of "Tube 2" was obtained through the S-curve trend model and cumulative sum control chart graph.
  
  
  RESULTS
  In the comparison of the initial and final sessions, every parameter such as mean time, collision, and critical errors was significantly improved. The repeat count for acquiring sufficient proficiency was 74 times, and the total amount of time invested for this was calculated as about 4 hours. The mean time at the plateau of the learning curve was 138 seconds.
  
  
  CONCLUSIONS
  Our study showed that the robotic virtual reality simulator (dV-Trainer) can yield sufficient improvement in technical performance in the "Tube 2" task within 4 hours. The simulator improves the technical surgical performance, but the development of more applications to reflect actual surgical situations is needed to improve and maximize the usefulness of the simulator.
authors.affiliation: Department of Urology, Korea University School of Medicine, Seoul, Korea.
authors.forename: Sung Gu
authors.initials: SG
authors.lastname: Kang
authors.forename: Kyung Sook
authors.initials: KS
authors.lastname: Yang
authors.forename: Young Hwii
authors.initials: YH
authors.lastname: Ko
authors.forename: Seok Ho
authors.initials: SH
authors.lastname: Kang
authors.forename: Hong Seok
authors.initials: HS
authors.lastname: Park
authors.forename: Jeong Gu
authors.initials: JG
authors.lastname: Lee
authors.forename: Je Jong
authors.initials: JJ
authors.lastname: Kim
authors.forename: Jun
authors.initials: J
authors.lastname: Cheon
mesh_terms: D002983:Clinical Competence; D003198:Computer Simulation; D004504:Education, Medical, Undergraduate; D006801:Humans; D059032:Learning Curve; D011597:Psychomotor Performance; D012371:Robotics / Q000193:education*; D014584:User-Computer Interface