Integrating Learned Retraction and Model-Based Resection Planning for Automated Central Airway Obstruction Removal

Abstract

Surgeon shortages have driven significant recent advances in robotic surgical assistance. To progress toward surgical automation, we address transoral central airway obstruction (CAO) removal, selecting this procedure for its relatively constrained environment and clear clinical objectives. Prior work has demonstrated successful CAO removal with single-port, minimally invasive robotic systems, making it a promising target for autonomy. We present a new autonomous system designed for CAO removal that integrates a learning-based tissue retraction policy with a model-based resection planner, deployed on a bimanual robotic platform with vision guidance. While our long-term motivation is minimally invasive intervention, we evaluate our approach in an open-surgery setting, enabling rapid development and testing. We evaluate our system in 12 ex vivo CAO removal procedures running at three distinct levels of autonomy. Our results indicate that the learned retraction policy performs similarly or better than a manually designed heuristic, with a mean surgeon-rated score (1-10) of $8.0{\pm }1.6$ compared to $6.8{\pm }2.5$ , and a mean percent tumor volume removal of $113.9{\pm }18.8$ % compared to $111.9{\pm }13.3$ %. The model-based resections performed best when supervised, resulting in less tissue damage and better cutting accuracy. Furthermore, our approach produced clinically successful outcomes in all eight supervised autonomous trials. Notably, in one case, the system achieved complete tumor removal without any human intervention. Our overall findings underscore the importance of high-level supervision, revealing targets for future automation.