NSF Award
2430998
Robotic-assisted surgery has the potential to offer enhanced overall surgical performance and greater precision compared to traditional surgical methods. However, surgeons’ mental workload remains a concern in robotic-assisted surgery due to increased complexity of operations, leading to unexpected human errors and unsatisfactory surgical outcomes. As robotic-assisted surgery rapidly advances with more complex technology, it is critical to prevent surgeons’ mental overload to ensure surgical task performance and patient safety. Neuro-adaptive technology represents an innovative solution for reducing human mental workload by enabling the context-awareness of robots to offer adaptive interventions within response to variations in human cognitive states. However, the adoption of the neuro-adaptive technology in robotic-assisted surgery remains largely unexplored. This gap highlights a fundamental research opportunity in understanding the advantages and limitations of neuro-adaptive technology to enhance surgical outcomes. This EArly-concept Grant for Exploratory Research (EAGER) grant supports research to design neuro-adaptive technology for robotic-assisted surgery. Introducing such an innovative technology to robotic-assisted surgery has the potential to transform traditional teleoperation into a more collaborative human-robot interaction. This, in turn, has the potential to improve patient health, identify and mitigate cognitively demanding procedures or operative conditions, and to reduce costs associated with adverse patient outcomes.<br/><br/>This research aims to design neuro-adaptive robotic-assisted surgery by enabling the robot's awareness of the surgical context, with the aim of understanding: (1) how to monitor different surgeons’ workload levels, (2) how to understand the cause of such workload, and (3) how to perform interventions. An artificial intelligence-powered multi-sensing system will be investigated to monitor workload levels on a personal basis for surgeons with varying skill levels. A context-awareness architecture that synthesizes visual and auditory data will be used to identify the cause of mental overload and initiate proper interventions and a prototype of the researched neuro-adaptive technology will be designed and validated. By leveraging multi-modal sensor data, human factors modeling, and artificial intelligence, the ultimate goal of this project is to refine the implementation of these life-saving remote surgery techniques, ensuring that they are more effective, adaptable, and safe.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
