Surgical task expertise detected by a selforganizing neural network map

Abstract

Individual grip force profiling of bimanual simulator task performance of experts and novices using a robotic control device designed for endoscopic surgery permits defining benchmark criteria that tell true expert task skills from the skills of novices or trainee surgeons. Here we show that grip variability in a true expert and a complete novice executing a robot-assisted surgical simulator task reveal statistically significant differences as a function of task expertise, predicted by the output metric of a SelfOrganizing neural network Map (SOM) with a bio-inspired functional architecture that maps the functional connectivity of the somatosensory neural networks of the primate brain.