Knowledge elicitation for performance assessment in a computerized surgical training system

Mario Riojas, Chuan Feng, Allan Hamilton, Jerzy Rozenblit

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Effective training is the key to minimizing the dangers of minimally invasive surgery (MIS). At present, the assessment of laparoscopic skills relies on the expertise of senior surgeons. The judgment is typically based on and expressed in ordinal variables that can take values such as low, medium, high or other comparable terms. This limited assessment, along with the lack of expert surgeons' metacognitive awareness of how the judgment process takes place, results in imprecise rules for the evaluation of laparoscopic surgical skills. In this work, we present the knowledge elicitation process to model the performance metrics and the rules involved in the assessment of minimally invasive surgical skills. We have implemented a scoring system for the evaluation of laparoscopic skills based on five performance metrics capable of distinguishing between four proficiency levels while providing a quantitative score. Our assessment model is based on fuzzy logic, so that it is easier to mimic the judgment that is already performed by experienced surgeons. The presented framework was empirically validated using the performance data of 38 subjects belonging to five groups: non-medical students, medical students with no previous laparoscopic training, medical students with some training, residents, and expert surgeons.

Original languageEnglish (US)
Pages (from-to)3697-3708
Number of pages12
JournalApplied Soft Computing Journal
Issue number4
StatePublished - Jun 2011


  • Fuzzy logic
  • Knowledge elicitation
  • Membership functions
  • Minimally invasive surgery (MIS)
  • Objective assessment
  • Surgical training systems

ASJC Scopus subject areas

  • Software


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