The ranking and evaluation of surgeons surgical skills is an important factor in order to be able to appropriately assign patient cases according to the necessary level of surgeon competence, in addition to helping towards pinpointing the specific clinicians within the surgical cohort who require further developmental training. One of the more frequent means of surgical skills evaluation, has been seen to be via a qualitative assessment of a surgeons portfolio alongside other supporting pieces of information-a process of which is rather subjective.

The contribution presented as part of this paper, involves the use of a set of Delsys Trigno EMG wearable sensors which track and record the muscular activation patterns of a surgeon during a surgical procedure alongside computationally driven Artificial Intelligence(AI) methods towards the differentiation and ranking of the surgical skills of a clinician in a quantitative fashion. The participants for the research involved novice level surgeons, intermediate level surgeons and expert level surgeons in various simulated surgical cases. The results showed that the monitoring of a set of key anatomical muscles during the simulated surgical cases, can allow for an effective differentiation of a surgeons skillset based on an AI prediction.