title: Objective measures for longitudinal assessment of robotic surgery training.
abstract: OBJECTIVE
  Current robotic training approaches lack the criteria for automatically assessing and tracking (over time) technical skills separately from clinical proficiency. We describe the development and validation of a novel automated and objective framework for the assessment of training.
  
  
  METHODS
  We are able to record all system variables (stereo instrument video, hand and instrument motion, buttons and pedal events) from the da Vinci surgical systems using a portable archival system integrated with the robotic surgical system. Data can be collected unsupervised, and the archival system does not change system operations in any way. Our open-ended multicenter protocol is collecting surgical skill benchmarking data from 24 trainees to surgical proficiency, subject only to their continued availability. Two independent experts performed structured (objective structured assessment of technical skills) assessments on longitudinal data from 8 novice and 4 expert surgeons to generate baseline data for training and to validate our computerized statistical analysis methods in identifying the ranges of operational and clinical skill measures.
  
  
  RESULTS
  Objective differences in operational and technical skill between known experts and other subjects were quantified. The longitudinal learning curves and statistical analysis for trainee performance measures are reported. Graphic representations of the skills developed for feedback to the trainees are also included.
  
  
  CONCLUSIONS
  We describe an open-ended longitudinal study and automated motion recognition system capable of objectively differentiating between clinical and technical operational skills in robotic surgery. Our results have demonstrated a convergence of trainee skill parameters toward those derived from expert robotic surgeons during the course of our training protocol.
authors.affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. rajesh@jhu.edu
authors.forename: Rajesh
authors.initials: R
authors.lastname: Kumar
authors.forename: Amod
authors.initials: A
authors.lastname: Jog
authors.forename: Balazs
authors.initials: B
authors.lastname: Vagvolgyi
authors.forename: Hiep
authors.initials: H
authors.lastname: Nguyen
authors.forename: Gregory
authors.initials: G
authors.lastname: Hager
authors.forename: Chi Chiung Grace
authors.initials: CC
authors.lastname: Chen
authors.forename: David
authors.initials: D
authors.lastname: Yuh
mesh_terms: D000704:Analysis of Variance; D001331:Automation; D002983:Clinical Competence; D016000:Cluster Analysis; D004503:Education, Medical, Graduate; D006801:Humans; D059032:Learning Curve; D008137:Longitudinal Studies; D009048:Motor Skills; D015203:Reproducibility of Results; D012371:Robotics / Q000193:education*; D060388:Support Vector Machine; D025321:Surgery, Computer-Assisted / Q000193:education*; D013647:Task Performance and Analysis; D013997:Time Factors; D014481:United States