Abstract. In the context of minimally invasive surgery, clinical risks are highly associated with surgeons' skill in manipulating surgical tools and their knowledge of the closed anatomy. A quantitative surgical skill assessment can reduce faulty procedures and prevent some surgical risks. In this paper focusing on sinus surgery, we present two methods to identify both skill level and task type by recording motion data of surgical tools as well as the surgeon's eye gaze location on the screen. We generate a total of 14 discrete Hidden Markov Models for seven surgical tasks at both expert and novice levels using a repeated k-fold evaluation method. The dataset contains 95 expert and 139 novice trials of surgery over a cadaver. The results reveal two insights: eye-gaze data contains skill related structures; and adding this info to the surgical tool motion data improves skill assessment by 13.2% and 5.3% for expert and novice levels, respectively. The proposed system quantifies surgeon's skill level with an accuracy of 82.5% and surgical task type of 77.8%.
Ahmidi et al. REF used a combination of eye tracking and tool position to classify between expert and novice surgeons in endoscopic sinus surgery with an accuracy of 0.825.
Surgical task and skill classification from eye tracking and tool motion in minimally invasive surgery
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Computer Science Medicine