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

Tis study investigated whether parameters derived from hand motions of expert and novice surgeons accurately and objectively refect laparoscopic surgical skill levels using an artifcial intelligence system consisting of a three-layer chaos neural network. Sixtyseven 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 afer seven trials with a training dataset of 38 surgeons, with a correct judgment ratio of 0.99. Te 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 artifcial intelligence system distinguished between expert and novice surgeons among surgeons with unknown skill levels.