Autonomous Robot-Assisted Ureteroscopy (ARA-URS) for the Treatment of Kidney Stones
Sarvesh Saini, Julio Ojalvo, Jonathan Katz, Ubbo Visser
AI summary
Problem
Manual ureteroscopy suffers from a steep learning curve, suboptimal stone-free rates, and significant surgeon radiation exposure, while existing robotic assistants face adoption barriers due to cost and complexity.
Approach
The authors developed a 3-DOF robotic actuator for a semi-rigid ureteroscope and paired it with a physics-based digital twin in NVIDIA Isaac Sim to generate synthetic training data and validate autonomous navigation algorithms.
Key results
- Designed and prototyped a 3-DOF ARA-URS system with a Wolf RIWO digital ureteroscope
- Validated a high-fidelity digital twin in NVIDIA Isaac Sim that accurately replicates physical kinematics
- Achieved in-air tip positioning RMSE of ~1.87 mm in simulation and ~5.03 mm in real-world testing
- Demonstrated autonomous stone targeting with 88.77% average coverage in simulation and 59.04% in benchtop models
Why it matters
Offers a scalable, simulation-driven platform to refine surgical automation, reduce surgeon radiation exposure, and accelerate the clinical adoption of robotic ureteroscopy.
Abstract
We present a supervised autonomous robot- assisted ureteroscopy (ARA-URS) system for the treatment of kidney stones, integrated with a digital-twin (DT). A three degrees of freedom (3-DOF) robotic system was developed to actuate a Wolf disposable ureteroscope, enabling the ARA-URS to autonomously position the ureteroscope for laser lithotripsy procedures. The DT of the robotic system was developed to enable the generation of diverse synthetic intraoperative scenarios, which are used to train vision and control models for improved precision in ARA-URS. By mapping joint motion to virtual counterparts via precise physics simulation, the system ensures realistic representation and reliable validation. This framework’s performance was assessed with particular focus on endoscopic tip positioning. Initial in-air simulation experiments demonstrated root mean square error (RMSE) values of (1.871, 1.725, 1.194) mm in the x-, y-, and z-directions, respectively, computed as the deviation between the desired laser target position and the achieved ureteroscope tip position. Corresponding real-world experiments yielded RMSE values of (5.029, 3.919, 6.681) mm. The comparison between simulated and physical experiments indicates that the DT is able to reproduce the motion behavior of the physical system with good agreement. Further benchtop and simulation experiments demonstrated the system’s capacity for stone targeting (quanti- fied by the percentage of the image occluded by stone). In digital simulation, the ureteroscope achieved 88.77% average stone area coverage, while in the benchtop model, coverage averaged 59.04%. Together, this proof of concept highlights the potential of DT technology in robotic-assisted URS, offering a scalable and interactive platform for refining surgical techniques.