title: Computer-aided feedback of surgical knot tying using optical tracking.
abstract: BACKGROUND
  Quantifying the information content of hand motion during surgical knot tying using information theory based entropy measures enables the comparison of different groups: novice and expert. We hypothesized that complexity would differ between the 2 groups and predicted based on motor learning models that complexity/information would reduce with increased expertise.
  
  
  METHODS
  Six degrees of freedom hand-motion data during surgical knot tying were acquired using an infrared optical hand tracking device. Multiple data samples were obtained from 2 groups: novice (third-year medical students) and expert (attending surgeons). After preprocessing each knot tying data sample into a binary symbolic time series, 3 nonlinear complexity measures were calculated: Lempel Ziv complexity, Shannon entropy, and Renyi entropy. The Shannon and Renyi entropies were calculated using a word length of 6. A Student t test was used to test whether the 2 groups were from the same population when using these entropy measures, applying a p value of 0.05 to reject the null hypothesis.
  
  
  RESULTS
  The expert surgeons were found to have less complex patterns of motion compared with the novice group. This finding was statistically significant using Lempel Ziv complexity (p = 0.004), Shannon entropy (p = 0.006), and Renyi entropy with q = 2 (p = 0.006). Using Renyi entropy with q = 0.5, the 2 groups were not significantly different (p = 0.26).
  
  
  CONCLUSIONS
  The ability to separate novice from expert populations during surgical knot tying using information theory entropy measures could form the basis of a low-cost educational tool to provide feedback and to assess skill acquisition using low-fidelity bench models.
authors.affiliation: Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. robert_watson@med.unc.edu
authors.forename: Robert Anthony
authors.initials: RA
authors.lastname: Watson
mesh_terms: D000328:Adult; D002983:Clinical Competence; D003194:Computer-Assisted Instruction; D004504:Education, Medical, Undergraduate; D056228:Feedback, Sensory; D005260:Female; D006785:Hospitals, University; D006801:Humans; D008297:Male; D008505:Medical Staff, Hospital / Q000706:statistics & numerical data; D008875:Middle Aged; D009657:North Carolina; D018794:Problem-Based Learning; D011597:Psychomotor Performance / Q000502:physiology; D036301:Qualitative Research; D013337:Students, Medical / Q000706:statistics & numerical data; D013536:Suture Techniques / Q000193:education*; D013647:Task Performance and Analysis; D013997:Time Factors