title: Automated video-based assessment of surgical skills for training and evaluation in medical schools.
abstract: OBJECTIVE
  Routine evaluation of basic surgical skills in medical schools requires considerable time and effort from supervising faculty. For each surgical trainee, a supervisor has to observe the trainees in person. Alternatively, supervisors may use training videos, which reduces some of the logistical overhead. All these approaches however are still incredibly time consuming and involve human bias. In this paper, we present an automated system for surgical skills assessment by analyzing video data of surgical activities.
  
  
  METHODS
  We compare different techniques for video-based surgical skill evaluation. We use techniques that capture the motion information at a coarser granularity using symbols or words, extract motion dynamics using textural patterns in a frame kernel matrix, and analyze fine-grained motion information using frequency analysis.
  
  
  RESULTS
  We were successfully able to classify surgeons into different skill levels with high accuracy. Our results indicate that fine-grained analysis of motion dynamics via frequency analysis is most effective in capturing the skill relevant information in surgical videos.
  
  
  CONCLUSIONS
  Our evaluations show that frequency features perform better than motion texture features, which in-turn perform better than symbol-/word-based features. Put succinctly, skill classification accuracy is positively correlated with motion granularity as demonstrated by our results on two challenging video datasets.
authors: Georgia Tech College of Computing, 801, Atlantic Drive, Atlanta, GA, 30332, USA. aneeqzia@gmail.com.
authors: Aneeq
authors: A
authors: Zia
authors: Georgia Tech College of Computing, 801, Atlantic Drive, Atlanta, GA, 30332, USA.
authors: Yachna
authors: Y
authors: Sharma
authors: Georgia Tech College of Computing, 801, Atlantic Drive, Atlanta, GA, 30332, USA.
authors: Vinay
authors: V
authors: Bettadapura
authors: Department of Surgery, Emory University, 1364 Clifton Road, NE, Atlanta, GA, 30322, USA.
authors: Eric L
authors: EL
authors: Sarin
authors: School of Computing Science, Newcastle University, Claremont Tower, Newcastle, UK.
authors: Thomas
authors: T
authors: Ploetz
authors: Georgia Tech College of Computing, 801, Atlantic Drive, Atlanta, GA, 30332, USA.
authors: Mark A
authors: MA
authors: Clements
authors: Georgia Tech College of Computing, 801, Atlantic Drive, Atlanta, GA, 30332, USA.
authors: Irfan
authors: I
authors: Essa
mesh_terms: D001331:Automation; D002983:Clinical Competence; D004503:Education, Medical, Graduate / Q000379:methods*; D004521:Educational Measurement / Q000379:methods*; D013502:General Surgery / Q000193:education*; D006801:Humans; D012577:Schools, Medical; D014741:Video Recording
keywords: Classification; Feature modeling; Surgical skill