Surgical Tool Handle Vibration-Based Drilling State Recognition During Hip Fracture Fixation

Pedicle drilling force control of a robotic surgical system via spine-soft tissue coupling model and parameters optimization

Bone drilling is a crucial operation in spinal fusion surgery that requires precise control of the applied force to ensure surgical safety. This manuscript aims to enhance the force servo performance of the orthopedic robot during automatic bone drilling operations. Firstly, an analytical model is introduced to describe the spinal mobility of the spine-soft tissue coupling structure. Then, the model is calibrated using force data obtained from stress relaxation tests. Next, optimal force controller parameters are determined through drilling force control simulations based on the identified model. The dynamic performance and robustness of the closed-loop control system are analyzed to ensure safe drilling procedures. Finally, bone drilling experiments are conducted in a force control mode to verify the effectiveness of the proposed method. The step drilling force response's steady-state error is less than 0.15 N, the relative control error is less than 3 %, and there is no noticeable force overshoot. The amplitude of the sinusoidal force response decays to -3 dB when the target force frequency is up to 3.49 rad/s, indicating a wide control bandwidth. These results demonstrate that the proposed method can rapidly and safely provide an adequate force servo to carry out automatic bone drilling.

Optimizing milling parameters based on full factorial experiment and backpropagation artificial neural network of lamina milling temperature prediction model

BACKGROUND

Milling operations of laminae in spinal surgery generate high temperatures, which can lead to thermal injury and osteonecrosis and affect the biomechanical effects of implants, ultimately leading to surgical failure.

OBJECTIVE

In this paper, a backpropagation artificial neural network (Bp-ANN) temperature prediction model was developed based on full factorial experimental data of laminae milling to optimize the milling motion parameters and to improve the safety of robot-assisted spine surgery.

METHODS

A full factorial experiment design were used to analyze the parameters affecting the milling temperature of laminae. The experimental matrixes were established by collecting the corresponding cutter temperature Tc and bone surface temperature Tb for the milling depth, feed speed and different bone densities. The Bp-ANN lamina milling temperature prediction model was constructed from experiment data.

RESULTS

Increasing milling depth increases bone surface and cutter temperature. Increasing feed speed had little effect on cutter temperature, but decreased bone surface temperature. Increasing bone density of laminae increased cutter temperature. The Bp-ANN temperature prediction model had best training results in the 10th epoch, and there is no overfitting (training set R= 0.99661, validation set R= 0.85003, testing set R= 0.90421, all temperature data set R= 0.93807). The goodness of fit R of Bp-ANN was close to 1, indicating that the predicted temperature was in good agreement with the experiment measurements.

CONCLUSION

This study can help spinal surgery-assisted robot to select appropriate motion parameters at different density bones to improve lamina milling safety.