Cobotic drilling assistant for orthopedic surgery using Gaussian process-based breakthrough detection.

PURPOSE: We introduce a cobot assisted cortical bone drilling, as applied in femoral, or clavicular fractures. This requires high accuracy, as minor deviations can cause complications through soft tissue penetration (STP). Human-robot collaboration (HRC) combines the surgeon's expertise with robotic stability and sensing accuracy. Thereby, the robot assistant passively guides the drill and prevents soft tissue penetration by detecting breakthroughs via integrated f/t and joint velocity sensors and stopping in place.

METHODS

A robotic arm that passively guides the surgeon during drilling and actively stops upon breakthrough. The detection algorithm is online and model-free, instead relying on online Gaussian process (GP) regression to adapt to varying drilling conditions. The system was evaluated in a user study with an experimental group of N=16 and a control group of N=17 participants, whereby STP was measured using a depth camera.

RESULTS

Femoral, ulnar, and generic bone samples were drilled with initially sharp 3.2 mm drill bits. Across all bone types, the experimental group outperformed the control group by 6.79 mm (p< 0.001) and undercut the reported STP for experienced surgeons, 5.1 mm by 2.54 mm (p< 0.02) for the generic bone case, despite the fact that only lay participants were tested in this study. Baseline algorithms such as thresholding were also outperformed by the proposed method.

CONCLUSION

These findings provide evidence toward the potential of HRC and model online breakthrough detection methods to increase safety in orthopedic drilling and represent a promising step toward future clinical implementation and surgical training applications.