Thermal imaging helps robots cut tumors with less damage
Velocity Optimization via Thermal Imaging Enhances Autonomous Tumor Resection
Robotics
Summary
Cutting tumors with robots can accidentally harm healthy tissue because it's hard to control how fast and hot the tool moves. The authors designed a system called ThERMO that uses real-time heat images to adjust the cutting speed automatically. This makes cuts more precise and reduces heat damage without slowing down the surgery. The approach was tested on pig tissue and worked better than previous fixed-speed methods.
What this means in practice
- •For robotic surgery teams: Improve robot-assisted tumor removal by reducing heat damage and increasing cut precision using real-time thermal feedback control.
- •For medical device developers: Develop electrosurgical robots with closed-loop thermal control to optimize cutting speed automatically and enhance surgical outcomes.$Commercial implications: Enables creation of advanced surgical robots that minimize collateral tissue damage, improving marketable safety and performance features.
Authors
Naveed D. Riaziat, Jiawei Ge, Ethan Kilmer, Michael W. Mikula, Paul Maria Scheikl, Michael Kam, Leila J. Mady, Axel Krieger, Jeremy D. Brown
Abstract
Objective: To demonstrate and evaluate THERMO, a thermal-velocity optimization algorithm for autonomous electrosurgery, in ex vivo porcine tissue using the UR10e-based ASTR system. Methods: Thermography for Electrosurgical Rate Modulation via Optimization (ThERMO), a closed-loop algorithm that uses real-time thermal imaging data to minimize thermal injury while ensuring accurate cut margins, was tested on ex vivo porcine tissue. Histological analysis was performed to detect thermal injury extent at a cellular level, and 3D photogrammetric measurement was used to verify margin accuracy. ThERMO's performance was compared against two previously used fixed velocity methods. Results: ThERMO outperformed both fixed velocity comparison methods without requiring data-driven training or iterative tuning, reducing thermal injury by up to 52%, improving cut accuracy by up to 59%, and maintaining task completion speed. Conclusions: Closed-loop thermal feedback via ThERMO improves autonomous robotic electrosurgery by simultaneously reducing thermal injury and improving margin accuracy, reinforcing its value over fixed velocity approaches for tumor resection applications. Significance: This work advances autonomous robotic tumor resection by demonstrating that real-time closed-loop thermal feedback control can substantially reduce collateral tissue damage and improve surgical margin accuracy.