Modelling and evaluation of surgical performance using hidden Markov models.
Minimally invasive surgery has become very widespread in the last ten years. Since surgeons experience difficulties in learning and mastering minimally invasive techniques, the development of training methods is of great importance. While the introduction of virtual reality-based simulators has introduced a new paradigm in surgical training, skill evaluation methods are far from being objective. This paper proposes a method for defining a model of surgical expertise and an objective metric to evaluate performance in laparoscopic surgery. Our approach is based on the processing of kinematic data describing movements of surgical instruments. We use hidden Markov model theory to define an expert model that describes expert surgical gesture. The model is trained on kinematic data related to exercises performed on a surgical simulator by experienced surgeons. Subsequently, we use this expert model as a reference model in the definition of an objective metric to evaluate performance of surgeons with different abilities. Preliminary results show that, using different topologies for the expert model, the method can be efficiently used both for the discrimination between experienced and novice surgeons, and for the quantitative assessment of surgical ability.
Megali
Giuseppe
G
CRIM Lab, Scuola Superiore Sant' Anna, 56025 Pisa, Italy. peppe@crim.sssup.it
Sinigaglia
Stefano
S
Tonet
Oliver
O
Dario
Paolo
P
D003198:Computer Simulation; D003194:Computer-Assisted Instruction / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D010535:Laparoscopy / Q000379:methods*; D008390:Markov Chains; D008954:Models, Biological; D011361:Professional Competence; D025321:Surgery, Computer-Assisted / Q000379:methods*; D013647:Task Performance and Analysis; D014584:User-Computer Interface