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

Munenori Uemura; Morimasa Tomikawa; Tiejun Miao; Ryota Souzaki; Satoshi Ieiri; Tomohiko Akahoshi; Alan K. Lefor; Makoto Hashizume

doi:10.1155/2018/9873273

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

Munenori Uemura, Morimasa Tomikawa, Tiejun Miao, Ryota Souzaki, Satoshi Ieiri, Tomohiko Akahoshi, Alan K. Lefor, Makoto Hashizume

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

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.

Original language	English
Article number	9873273
Journal	Computational and Mathematical Methods in Medicine
Volume	2018
DOIs	https://doi.org/10.1155/2018/9873273
Publication status	Published - 2018

All Science Journal Classification (ASJC) codes

Modelling and Simulation
Biochemistry, Genetics and Molecular Biology(all)
Immunology and Microbiology(all)
Applied Mathematics

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Feasibility of an AI-Based Measure of the Hand Motions of Expert and Novice Surgeons. / Uemura, Munenori; Tomikawa, Morimasa; Miao, Tiejun; Souzaki, Ryota; Ieiri, Satoshi; Akahoshi, Tomohiko; Lefor, Alan K.; Hashizume, Makoto.

In: Computational and Mathematical Methods in Medicine, Vol. 2018, 9873273, 2018.

Research output: Contribution to journal › Article › peer-review

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abstract = "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.",

author = "Munenori Uemura and Morimasa Tomikawa and Tiejun Miao and Ryota Souzaki and Satoshi Ieiri and Tomohiko Akahoshi and Lefor, {Alan K.} and Makoto Hashizume",

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AU - Uemura, Munenori

AU - Tomikawa, Morimasa

AU - Miao, Tiejun

AU - Souzaki, Ryota

AU - Ieiri, Satoshi

AU - Akahoshi, Tomohiko

AU - Lefor, Alan K.

AU - Hashizume, Makoto

N1 - Funding Information: This work was supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and the Japan Society for the Promotion of Science (JSPS) KAK-ENHI (Grant nos. 26108010, 16H05882, and 17K01414). The Publisher Copyright: © 2018 Munenori Uemura et al.

PY - 2018

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N2 - 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.

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

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