Assessing surgeons' skill level in laparoscopic cholecystectomy using eye metrics

Abstract

Laparoscopic surgery has revolutionised state of the art in surgical health care. However, its complexity puts a significant burden on the surgeon's cognitive resources resulting in major biliary injuries. With the increasing number of laparoscopic surgeries, it is crucial to identify surgeons' cognitive loads (CL) and levels of focus in real time to give them unobtrusive feedback when detecting the suboptimal level of attention. Assuming that the experts appear to be more focused on attention, we investigate how the skill level of surgeons during live surgery is reflected through eye metrics. Forty-two laparoscopic surgeries have been conducted with four surgeons who have different expertise levels. Concerning eye metrics, we have used six metrics which belong to fixation and pupillary based metrics. With the use of mean, standard deviation and ANOVA test we have proven three reliable metrics which we can use to differentiate the skill level during live surgeries. In future studies, these three metrics will be used to classify the surgeons' cognitive load and level of focus during the live surgery using machine learning techniques.