Ex vivo breach detection using electrical conductivity during robotic pedicle drilling in the spine
Jorge Andrés Pérez Velásquez, François Teyssere, Thibault Chandanson, Quentin Grimal, Brahim Tamadazte
Abstract
Purpose: Pedicle screw placement is technically demanding in scoliosis treatment. High precision is required due to limited visibility, anatomical variability, and the risk of complications. Although robotic systems assist CT-based planning and execution, they still rely on ionizing intraoperative imaging and complex registration. This study proposes robotic pedicle drilling with real-time preventive breach detection using electrical bioimpedance sensing. Methods: We developed a robotic approach combined with a pedicle-drilling tool equipped with a proprioceptive electrical bioimpedance sensor developed by SpineGuard. A real-time detection algorithm was designed to analyze the electrical bioimpedance signal during drilling and identify abrupt changes in conductivity associated with potential breaches towards the spinal canal. The method operates without external devices or sensors. Results: The ex vivo experiments showed that the proposed method prevented breaches in 100 of the 51 drilling cases. These findings demonstrate the system's ability to detect potentially hazardous events during drilling and to stop the procedure before. The ex vivo experiments demonstrated that the proposed method prevented breaches in all 51 drilling cases. Conclusions: This work demonstrates the feasibility of robotic pedicle drilling with electrical bioimpedance sensing for real-time breach prevention. Using only the tool signal, the method eliminates the need for external sensing systems and supports safer pedicle screw placement.
Create a lesson
Related papers
Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
Yen-Jen Wang, Haozhe Jiang, Shuying Deng et al.
InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation
Zhuo Lin, Sirui Xu, Liuyu Bian et al.
Watch, Infer, Coordinate: Inferring Robot Partner Constraints for Zero-Shot Coordination
Suyu Ye, Zheyuan Zhang, Vaishnav Tadiparthi et al.
DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication
Hanchu Zhou, Dechen Gao, Hang Wang et al.
SkeleWAM: Skeleton World-Action Modeling for Efficient Robotic Manipulation
Juyi Sheng, Hua Wang, Mengyuan Liu
GlassGuard: Verified Glass Plane Mapping for Robot Navigation
Hanwen Guo, Zhengzhi Lin, Yusen Xie et al.