Automating Endoscopic Control
Abstract: Minimally invasive surgery systems, such as the da Vinci system, have demonstrated superior accuracy, reduced blood loss, and shorter hospital stays compared to Standard Laparoscopic Techniques. However, in robotic-assisted surgeries (RAS), the surgeon must frequently switch between controlling surgical arms and adjusting the Endoscope camera Manipulator (ECM), often resulting in sub-optimal endoscope views. This can increase the surgeon’s cognitive load and potentially impact surgical outcomes. To address this challenge, we propose an automated approach for Endoscope control to consistently provide the optimal view for the surgeon during laparoscopic procedures, using eye tracking and fixation points. By correlating ECM motion with the surgeon’s eye tracking through an Augmented Reality (AR) headset, we construct a finite state machine (FSM). Our study aims to compare the Task Load Index (TLX) of surgeons with and without ECM automation. We discuss our methodology, present results, acknowledge limitations, and outline future steps towards complete automation.
Advisor: Professor Gregory Fischer