Computer Vision and Fuzzy-Neural Systems
Computer Vision and Fuzzy-Neural Systems
Guidelines for eliciting expert judgment as probabilities or fuzzy logic
Fuzzy logic and probability applications
Fuzzy set theory for performance evaluation in a surgical simulator
Presence: Teleoperators and Virtual Environments
Fuzzy Logic-Based Performance Assessment in the Virtual, Assistive Surgical Trainer (VAST)
ECBS '08 Proceedings of the 15th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems
Fuzzy Modeling and Control
Models and techniques for computer aided surgical training
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part II
Application of simulation techniques in a virtual laparoscopic laboratory
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part II
Automatic linguistic reporting in driving simulation environments
Applied Soft Computing
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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.