Abstract
Oral cavity cancer, a common head and neck cancer, is typically treated through precise tumor excision via electrosurgery. Autonomous robotic electrosurgery has demonstrated the potential to achieve more accurate and consistent resection margins compared to manual methods, thereby improving surgical outcomes. However, current autonomous systems face challenges in tracking tissue deformation during electrosurgical cutting due to unpredictable and complex soft tissue dynamics. Failure to monitor and adapt to tissue deformation can significantly compromise resection precision. This paper presents an autonomous closed-loop robotic electrosurgery system to enhance surgical precision via 3D tissue tracking and image-based feedback control utilizing a Red Green Blue - Depth (RGB-D) sensor. The developed 3D tissue tracker employs CoTracker, a deep learning-based model for markerless tracking, complemented by a tool-occlusion algorithm to achieve tissue deformation tracking with no prior knowledge of the tissue model. The estimated deformation is fed into a fuzzy logic controller, which dynamically adjusts the cutting velocity to minimize cutting error during electrosurgery. The system's efficacy was validated using ex vivo porcine tongues, demonstrating a 55% reduction in average cutting error (from 1.2 mm to 0.54 mm, p < 0.001 ) in closed-loop operations (N=6) compared to open-loop cutting without feedback control (N=3). The results demonstrate the effectiveness of image-based closed-loop control in improving margin accuracy, a key factor in reducing the likelihood of cancer recurrence.