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 | |
| Publication status | Published - Jan 1 2018 |
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All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Applied Mathematics
Cite this
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, 01.01.2018.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Feasibility of an AI-Based Measure of the Hand Motions of Expert and Novice Surgeons
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
PY - 2018/1/1
Y1 - 2018/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85046283756&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046283756&partnerID=8YFLogxK
U2 - 10.1155/2018/9873273
DO - 10.1155/2018/9873273
M3 - Article
C2 - 29686724
AN - SCOPUS:85046283756
VL - 2018
JO - Computational and Mathematical Methods in Medicine
JF - Computational and Mathematical Methods in Medicine
SN - 1748-670X
M1 - 9873273
ER -