Controlling Cognitive Demands With Semi-Autonomous Suction Framework for Robotic-Assisted Surgery

Robotic-assisted minimally invasive surgery (RMIS) has been increasing steadily since its introduction in the early 2000s and now has become a medical standard in multiple surgical specialties. In RMIS, the leading surgeon teleoperates a surgical robot from a console distant from the patient. While on the patient side, there is at least one surgical assistant supporting the procedure. One of the most important tasks done by the surgical assistant is blood suction and irrigation. This task is critical to maintain a clear view of the surgical field and avoid contaminations and infections. When several tasks are competing for the surgical assistant’s attention, taking care of blood suction implies leaving unattended other assistive tasks, such as exchanging robotic instruments and handling sutures. An alternative approach to handle bleeding events is having the leading surgeon teleoperate the suction tool. Likewise, this leads to less attention allocated to the patient and an increase in their cognitive load. To alleviate this problem, we proposed a semi-autonomous suction assistant to release the main surgeon of the blood suction task and the additional cognitive demands associated with it. At the heart of this system, there is a deep learning algorithm that segments and identifies the location of blood accumulations allowing the autonomous system to navigate through the surgical field. Additionally, an augmented reality (AR) and a real-time cognitive workload assessment module were developed to improve human-robot work dynamics. A user study indicated that the autonomous system resulted in improved completion time and improved human-robot collaboration fluency when compared against a condition of manual teleoperation of the suction tool. Overall, the experiments’ results show how objective and real-time cognitive load assessments can be used together with surgical autonomy to enhance RMIS surgical outcomes.