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Twenty Years Beyond the Turing Test: Moving Beyond the Human Judges Too
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Automatic supervision of gestures to guide novice surgeons during training
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Abstract:
The final publication is available at Springer via http://dx.doi.org/10.1007/s00464-013-3285-9 ; 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. ; Monserrat, C.; Lucas, A.; Hernández Orallo, J.; Rupérez Moreno, MJ. (2014). Automatic supervision of gestures to guide novice surgeons during training. Surgical Endoscopy. 28(4):1360-1370. doi:10.1007/s00464-013-3285-9 ; S ; 1360 ; 1370 ; 28 ; 4 ; Ericsson KA (ed) (2009) Development of professional expertise: toward measurement of expert performance and design of optimal learning environments. Cambridge University Press, New York ; McGaghie WC (2008) Research opportunities in simulation-based medical education using deliberate practice. Acad Emerg Med 15:995–1001 ; Ericsson KA (2008) Deliberate practice and acquisition of expert performance: a general overview. Acad Emerg Med 15:988–994 ; Issenberg SB, McGaghie WC, Petrusa ER et al (2005) Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach 27:10–28 ; Porte MC, Xeoulis G, Reznick RK, Dubrowski A (2007) Verbal feedback from an expert is more effective than self-accessed feedback about motion efficiency in learning new surgical skills. Am J Surg 193:105–110. doi:10.1016/j.amjsurg.2006.03.016 ; Hall PAV, Dowling GR (1980) Approximate string matching. ACM computing surveys (CSUR) 18(2):381–402. doi:10.1145/356827.356830 ; Stylopoulos N, Cotin S, Maithel SK et al (2004) Computer-enhanced laparoscopic training system (CELTS): bridging the gap. Surg Endosc 18(5):782–789. doi:10.3233/978-1-60750-938-7-336 ; 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 ; 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 ; Chmarra MK, Klein S, Winter JCF, Jansen FW, Dankelman J (2010) Objective classification of residents based on their psychomotor laparoscopic skills. Surg Endosc 24(5):1031–1039. doi:10.1007/s00464-009-0721-y ; Lin HC, Shafran I, Yuh D, Hager GD (2006) Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions. Comput Aided Surg 11(5):220–230. doi:10.3109/10929080600989189 ; Rosen J, Brown JD, Chang L, Sinanan MN, Hannaford B (2006) Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model. IEEE Trans Biomed Eng 53(3):399–413. doi:10.1109/TBME.2005.869771 ; Lahanas V, Loukas C, Nikiteas N, Dimitroulis D, Georgiou E (2011) Psychomotor skills assessment in laparoscopic surgery using augmented reality scenarios. 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Inf Control 64(1–3):100–118. doi:10.1016/S0019-9958(85)80046-2 ; Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv 33(1):31–88. doi:10.1145/375360.375365 ; Damerau FJ (1964) A technique for computer detection and correction of spelling errors. Commun ACM 7(3):171–176. doi:10.1145/363958.363994 ; 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 ; Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334. doi:10.1109/34.888718 ; Simbionix™, Lap Mentor™. simbionix.com. http://simbionix.com/simulators/lap-mentor/library-of-modules/basic-skills/ . Accessed 31 Jan 2013 ; Wagner RA, Fischer MJ (1974) Algorithms for approximate string matching. 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Keyword:
Endoscopy; Imaging & VR; LENGUAJES Y SISTEMAS INFORMATICOS; Technical computing; Technical human/robotic; Technical surgical; Technical training
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URL: https://doi.org/10.1007/s00464-013-3285-9
http://hdl.handle.net/10251/43933
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