Enhancing Surgical Precision in Autonomous Robotic Incisions via Physics-Based Tissue Cutting Simulation

In soft tissue surgeries, such as tumor resections, achieving precision is of utmost importance. Surgeons conventionally achieve this precision through intraoperative adjustments to the cutting plan, responding to deformations from tool-tissue interactions. This study examines the integration of physics-based tissue cutting simulations into autonomous robotic surgery to preoperatively predict and compensate for such deformations, aiming to improve surgical precision and reduce the necessity for dynamic adjustments during autonomous surgeries. This study adapts a real-to-sim-to-real workflow. Initially, the Autonomous System for Tumor Resection (ASTR) was employed to evaluate its accuracy in performing preoperatively intended incisions along the irregular contours of porcine tongue pseudotumors. Following this, a finite element analysis-based simulation, utilizing the Simulation Open Framework Architecture (SOFA), was developed and tuned to accurately mimic these tissue and incision interactions. Insights gained from this simulation were applied to refine the robot's path planning, ensuring a closer alignment of actual incisions with the initially intended surgical plan. The efficacy of this approach was validated by comparing surface incision precision on ex vivo porcine tongues, with the average absolute error reducing from 1.73mm to 1.46mm after applying simulation-driven path adjustments (p < 0.001). Additionally, our method not only demonstrated improvements in maintaining the intended cutting shapes and locations, with shape matching scores using Hu moments enhancing from 0.10 to 0.06 and centroid shifts decreasing from 2.09mm to 1.33mm, but it also potentially reduced the likelihood of adverse oncologic outcomes by preventing clinically suggested excessively close margins of 2.2mm. This feasibility study suggests that merging physics-based cutting simulations with autonomous robotic surgery could potentially lead to more accurate incisions.