Model-Based Compensation of Moving Tissue for State Recognition in Robotic-Assisted Pedicle Drilling
This addresses the critical safety issue of preventing tissue injury during spinal surgery when vertebrae are not stationary, though it is incremental as it builds on existing robotic-assisted drilling methods.
The paper tackled the problem of recognizing drilling states in moving vertebrae during robotic-assisted pedicle screw fixation by compensating for tissue displacement using a patient-specific motion prediction model, achieving a 95% success rate in experiments on porcine bone.
Drilling is one of the hardest parts of pedicle screw fixation, and it is one of the most dangerous operations because inaccurate screw placement would injury vital tissues, particularly when the vertebra is not stationary. Here we demonstrate the drilling state recognition method for moving tissue by compensating the displacement based on a simplified motion predication model of a vertebra with respect to the tidal volume. To adapt it to different patients, the prediction model was built based on the physiological data recorded from subjects themselves. In addition, the spindle speed of the drilling tool was investigated to find a suitable speed for the robotic-assisted system. To ensure patient safety, a monitoring system was built based on the thrusting force and tracked position information. Finally, experiments were carried out on a fresh porcine lamellar bone fixed on a 3-PRS parallel robot used to simulate the vertebra displacement. The success rate of the robotic-assisted drilling procedure reached 95% when the moving bone was compensated.