Evaluation of the 10-year history of a 2-day standardized laparoscopic surgical skills training program at Kyushu University

Tomikawa, Morimasa; Uemura, Munenori; Kenmotsu, Hajime; Konishi, Kozo; Ohuchida, Kenoki; Okazaki, Ken; Ieiri, Satoshi; Tanoue, Kazuo; Hashizume, Makoto

doi:10.1007/s00595-015-1227-y

Cited by 10 publications

(1 citation statement)

References 21 publications

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“…Participants in this study included 67 surgeons enrolled in a laparoscopic surgery training course held at the Kyushu University Training Center for Minimally Invasive Surgery [ 1 , 8 – 12 ]. All participating surgeons performed the skill assessment task, which was described previously [ 1 , 8 , 9 ]. None of the participants were included in our previous study [ 8 ], and 38 were enrolled in study 1 (optimization study of the AI system) and the remaining 29 in study 2 (validation study of the AI system).…”

Section: Methodsmentioning

confidence: 99%

Feasibility of an AI-Based Measure of the Hand Motions of Expert and Novice Surgeons

Uemura

Tomikawa

Tang³

et al. 2018

Computational and Mathematical Methods in Medicine

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This study investigated whether parameters derived from hand motions of expert and novice surgeons accurately and objectively reflect laparoscopic surgical skill levels using an artificial intelligence system consisting of a three-layer chaos neural network. Sixty-seven surgeons (23 experts and 44 novices) performed a laparoscopic skill assessment task while their hand motions were recorded using a magnetic tracking sensor. Eight parameters evaluated as measures of skill in a previous study were used as inputs to the neural network. Optimization of the neural network was achieved after seven trials with a training dataset of 38 surgeons, with a correct judgment ratio of 0.99. The neural network that prospectively worked with the remaining 29 surgeons had a correct judgment rate of 79% for distinguishing between expert and novice surgeons. In conclusion, our artificial intelligence system distinguished between expert and novice surgeons among surgeons with unknown skill levels.

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