Surgical task expertise detected by a self-organizing neural network map
This work addresses skill assessment in surgical training, but it is incremental as it applies an existing neural network method to a new domain-specific dataset.
The study tackled the problem of distinguishing expert from novice surgical skills by analyzing grip force variability during robot-assisted tasks, achieving statistically significant differences in predictions using a self-organizing neural network map.
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. Grip force 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. Here we show that the skill specific differences in local grip forces are predicted by the output metric of a Self Organizing neural network Map (SOM) with a bio inspired functional architecture that maps the functional connectivity of somatosensory neural networks in the primate brain.