A Novel Hybrid Ureteroscope Tracking for Robotic-Assisted Retrograde Intrarenal Surgery Via Recognition of Pathway with Lumen Identification
Jung-Min Han, Dong-Soo Kwon, Ki-Uk Kyung
AI summary
Problem
Surgeons performing robotic-assisted retrograde intrarenal surgery (RIRS) often experience disorientation and increased cognitive workload due to the lack of direct spatial feedback and difficulty tracking the ureteroscope's position within the kidney.
Approach
The method combines real-time robotic motion data with a deep learning-based lumen identification algorithm to continuously map the ureteroscope's position onto a 3D kidney centerline without requiring external sensors.
Key results
- 89.2% localization success rate for major calyx entry
- 84.1% localization success rate for minor calyx entry
- 0.26-second average computation time at bifurcation points
- 44.5% reduction in cognitive workload for novice surgeons
Why it matters
Improves surgical efficiency and spatial awareness, making robotic-assisted RIRS safer and more accessible for training and clinical practice.
Abstract
Resolving disorientation of the surgeon caused by wrong recognition of scope’s position, which often increases pro- cedural time and workload, remains a significant challenge in robotic-assisted retrograde intrarenal surgery (RIRS). This letter introduces a novel hybrid ureteroscope tracking algorithm that integrates low-latency lumen identification with robotic motion data to enhance intrarenal navigation. The system estimates the ureteroscope’s position on the centerline of the kidney by recogniz- ing its pathway. In validation tests using a 3D-printed phantom, the proposed method achieved an average localization success rate of 89.2% for major calyx entry and 84.1% for minor calyx entry, with an average computation time of 0.26 seconds, ensuring low-latency operation. Usability testing with ten novice participants demon- strated a 44.5% reduction in cognitive workload (NASA-TLX), improved task success rates, and reduced manipulation effort. These results indicate that the proposed tracking algorithm sig- nificantly enhances ureteroscope navigation, improving efficiency and reducing the surgeon’s cognitive load in robotic-assisted RIRS.