A neurotechnological aid for semi-autonomous suction in robotic-assisted surgery

Abstract

Adoption of robotic-assisted surgery has steadily increased as it improves the surgeon’s dexterity and visualization. Despite these advantages, the success of a robotic procedure is highly dependent on the availability of a proficient surgical assistant that can collaborate with the surgeon. With the introduction of novel medical devices, the surgeon has taken over some of the surgical assistant’s tasks to increase their independence. This, however, has also resulted in surgeons experiencing higher levels of cognitive demands that can lead to reduced performance. In this work, we proposed a neurotechnology-based semi-autonomous assistant to release the main surgeon of the additional cognitive demands of a critical support task: blood suction. To create a more synergistic collaboration between the surgeon and the robotic assistant, a real-time cognitive workload assessment system based on EEG signals and eye-tracking was introduced. A computational experiment demonstrates that cognitive workload can be effectively detected with an 80% accuracy. Then, we show how the surgical performance can be improved by using the neurotechnological autonomous assistant as a close feedback loop to prevent states of high cognitive demands. Our findings highlight the potential of utilizing real-time cognitive workload assessments to improve the collaboration between an autonomous algorithm and the surgeon.