A hidden markov model-based analysis framework using eye-tracking data to characterise re-orientation strategies in minimally invasive surgery

Natural Orifice Translumenal Endoscopic Surgery (NOTES) represents a new paradigm in minimally invasive surgery whereby an operator uses a flexible instrument to navigate within an anatomical space. The flexible instrument is introduced via a natural body orifice such as the mouth to perform an operative intervention controlled by the surgeon without making incisions in the anterior abdominal wall. It differs from laparoscopic (traditional keyhole) surgery in two main ways: (1) the instruments used in NOTES are not rigid as in laparoscopic surgery and not specifically designed for this application; and (2) there are no incisions in the anterior abdominal wall. Disorientation during NOTES is a significant problem because as in laparoscopic surgery the operator typically uses a 2D camera-monitor interface for visualisation by the camera at the site of the operation. Furthermore, the positional cue offered by the external component (outside the body) of the camera is absent, and camera navigation is more cumbersome. When the operator is disorientated, it is important to be able to re-orientate quickly to minimise potential surgical errors. It is hypothesised that when surgeons become disorientated, there exist discrete patterns in psychophysical behaviour which are associated with effective re-orientation, and that these patterns are recognisable. In this study, we examine visual re-orientation behaviour in 18 subjects using eye-tracker data in a model comprised of selective image manipulation of everyday objects in a box trainer. We characterise effective behaviour using a fixation sequence similarity-based hidden Markov model. We show that the output of this algorithm is reliable in differentiating visual behavioural sequences, and that there are specific behavioural patterns and strategies associated with successful re-orientation in this model. Good re-orientation strategy appears to rely on identification and focus on a central object within the scene and judging position of its surrounding peripheral objects, suggesting integration of both geometric and feature information in a systematic way. Using selective, inconsistent feature cues for re-orientation were associated with less effective performance.