C Monserrat1, A Lucas, J Hernández-Orallo, M José Rupérez. 1. LabHuman, Ciudad Politécnica de la Innovación, Universitat Politècnica de València, Cubo Azul, Edif. 8B, Acceso N, Camino de Vera s/n, 46022, Valencia, Spain, cmonserr@dsic.upv.es.
Abstract
BACKGROUND: Virtual surgery simulators enable surgeons to learn by themselves, shortening their learning curves. Virtual simulators offer an objective evaluation of the surgeon's skills at the end of each training session. The considered evaluation parameters are based on the analysis of the surgeon's gestures performed throughout the training session. Currently, this information is usually known by surgeons only at the end of the training session, but very limited during the training performance. In this paper, we present a novel method for automatic and interactive evaluation of the surgeon's skills that is able to supervise inexperienced surgeons during their training session with surgical simulators. METHODS: The method is based on the assumption that the sequence of gestures carried out by an expert surgeon in the simulator can be translated into a sequence (a character string) that should be reproduced by a novice surgeon during a training session. In this work, a string-matching algorithm has been modified to calculate the alignment and distance between the sequences of both expert and novice during the training performance. RESULTS: The results have shown that it is possible to distinguish between different skill levels at all times during the surgical training session. CONCLUSIONS: The main contribution of this paper is a method where the difference between an expert's sequence of gestures and a novice's ongoing sequence is used to guide inexperienced surgeons. This is possible by indicating to novices the gesture corrections to be applied during surgical training as continuous expert supervision would do.
BACKGROUND: Virtual surgery simulators enable surgeons to learn by themselves, shortening their learning curves. Virtual simulators offer an objective evaluation of the surgeon's skills at the end of each training session. The considered evaluation parameters are based on the analysis of the surgeon's gestures performed throughout the training session. Currently, this information is usually known by surgeons only at the end of the training session, but very limited during the training performance. In this paper, we present a novel method for automatic and interactive evaluation of the surgeon's skills that is able to supervise inexperienced surgeons during their training session with surgical simulators. METHODS: The method is based on the assumption that the sequence of gestures carried out by an expert surgeon in the simulator can be translated into a sequence (a character string) that should be reproduced by a novice surgeon during a training session. In this work, a string-matching algorithm has been modified to calculate the alignment and distance between the sequences of both expert and novice during the training performance. RESULTS: The results have shown that it is possible to distinguish between different skill levels at all times during the surgical training session. CONCLUSIONS: The main contribution of this paper is a method where the difference between an expert's sequence of gestures and a novice's ongoing sequence is used to guide inexperienced surgeons. This is possible by indicating to novices the gesture corrections to be applied during surgical training as continuous expert supervision would do.
Authors: N Stylopoulos; S Cotin; S K Maithel; M Ottensmeye; P G Jackson; R S Bardsley; P F Neumann; D W Rattner; S L Dawson Journal: Surg Endosc Date: 2004-05 Impact factor: 4.584
Authors: Julian J H Leong; Marios Nicolaou; Louis Atallah; George P Mylonas; Ara W Darzi; Guang-Zhong Yang Journal: Med Image Comput Comput Assist Interv Date: 2006