Automatic supervision of gestures to guide novice surgeons during training
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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.
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.
The results have shown that it is possible to distinguish between different skill levels at all times during the surgical training session.
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.
KeywordsImaging & VR Technical surgical Technical human/robotic Technical computing Technical training Endoscopy
The authors thank Dr. Francisco Dolz, Director of the Area of Clinical Simulation and Instructor of Clinical Simulation of the Hospital Universitari i Politècnic La Fe of Valencia (Spain), for his revision of the paper and his advice and suggestions. We also thank the anonymous reviewers for their comments, which have helped to improve the paper.
Dr. Carlos Monserrat, Alejandro Lucas, Dr. José Hernández, Dr. M. José Rupérez, and Dr. Mariano Alcañiz have no conflicts of interest or financial ties to disclose.
- 1.Ericsson KA (ed) (2009) Development of professional expertise: toward measurement of expert performance and design of optimal learning environments. Cambridge University Press, New YorkGoogle Scholar
- 8.Solis J, Oshima N, Ishii H, Matsuoka N et al (2009) Quantitative assessment of the surgical training methods with the suture/ligature training system WKS-2RII. In: IEEE international conference on robotics and automation, 2009 (ICRA ‘09), Kobe, pp 4219–4224. doi: 10.1109/ROBOT.2009.5152314
- 9.Lin Z et al (2010) Objective evaluation of laparoscopic surgical skills using Waseda bioinstrumentation system WB-3. In: IEEE international conference on robotics and biomimetics (ROBIO), Tianjin, pp 247–252. doi: 10.1109/ROBIO.2010.5723335
- 13.Lahanas V, Loukas C, Nikiteas N, Dimitroulis D, Georgiou E (2011) Psychomotor skills assessment in laparoscopic surgery using augmented reality scenarios. In: 17th international conference on digital signal processing (DSP), Corfu. doi: 10.1109/ICDSP.2011.6004893
- 14.Leong JJ et al (2006) HMM assessment of quality of movement trajectory in laparoscopic surgery. In: International conference on medical image computing and computer-assisted intervention (MICCAI’06), pp 752–759. doi: 10.3109/10929080701730979
- 21.Bergroth L, Hakonen H, Raita T (2000) A survey of longest common subsequence algorithms. In: Proceedings of the seventh international symposium on string processing information retrieval (SPIRE’00), A Coruña, p 39. doi: 10.1109/SPIRE.2000.878178
- 23.Simbionix™, Lap Mentor™. simbionix.com. http://simbionix.com/simulators/lap-mentor/library-of-modules/basic-skills/. Accessed 31 Jan 2013