Goal: In the context of robot-assisted laminectomy surgery, an analytical force model is introduced to guarantee procedural safety. The aim of the method is to intraoperatively monitor the cutting depth via modeling the milling status. Methods: The theoretical dynamic model for the surgical milling process is based on the flute geometry of the ball-end milling tool. A particle swarm optimization algorithm is exploited to calibrate the model using the local average force, and to validate it using the denoised dynamic force. A wear detection method based on the fast Fourier transform is proposed to determine the quality of the tool geometry and to avoid using worn tools, which may lead to imprecise and unsafe operations. Results: Milling experiments were performed on machined fresh bovine femur bones. The experimental results thus obtained from the mechanical model are in good accordance with the numerical model. The proposed method can monitor the current cutting depth with an accuracy of ±0.1 mm in regions located within the depth [0.8-1.2 mm], and ±0.2 mm within [1.2-1.6 mm]. Conclusion: The proposed model can successfully estimate the milling force and the cutting depth intraoperatively in experimental conditions. Significance: This approach has the potential to improve the safety of laminectomy operations in humans, and make it more accessible to younger surgeons by lowering the required manual skills threshold.

Pedicle drilling is an important step in pedicle screw fixation and the most significant challenge in this operation is how to determine a key point in the transition region between cancellous and inner cortical bone. The purpose of this paper is to find a method to achieve the recognition for the key point. After acquiring acoustic emission (AE) signals during the drilling process, this paper proposed a novel frequency distribution-based algorithm (FDB) to analyze the AE signals in the frequency domain after certain processes. Then we select a specific frequency domain of the signal for standard operations and choose a fitting function to fit the obtained sequence. Characters of the fitting function are extracted as outputs for identification of different bone layers. The results, which are obtained by detecting force signal and direct measurement, are given in the paper. Compared with the results above, the results obtained by AE signals are distinguishable for different bone layers and are more accurate and precise. The results of the algorithm are trained and identified by a neural network and the recognition rate reaches 84.2%. The proposed method is proved to be efficient and can be used for bone layer identification in pedicle screw fixation.

Robot-assisted decompressive laminectomy is a new strategy in clinical applications. However, a stable contact force between the bone-cutting device and lamina is needed to keep it working correctly, which may be affected by respiration and deformation. The surgeon can adapt to this dynamic process quickly, but the robot could cause a considerable force that may damage the patient. This paper proposes a compensation control method based on a respiration-spine model to first improve the stability of the contact force. The model is established based on human morphology and ventilator parameters for anaesthetised patients. The control method is a combination of a surgery sleeve and active fuzzy control to improve the robustness of the robot. The control of the sleeve is related to the thickness of the bone layer, which can be calculated from the image. Furthermore, the lower boundary of the lamina in the CT image is extracted as a safety constraint that can protect the spinal nerves. Finally, an experiment is conducted to verify the safety constraint and compare the changes in contact force with or without the control method. The statistical experiment shows that the control error is 2.47 N without the force control method, while the force control error is 0.223 N when the target control force is 2 N. The robot will hover on the surface of the spine after completing the laminectomy of the planned area. These results show that the robot can be controlled safely and stably.

When the spinal surgery robot assists the surgeon perform the surgery, the patient is prone on the operating table. However, due to the force of the surgical instruments on the spine, there is a corresponding deformation in the surgical field, which affects the accuracy of the operation. In order to improve the accuracy and safety of the operation, this paper reconstructs the three-dimensional model of the lumbar spine which includes the vertebral body and the intervertebral disc based on the CT scan data, and then the lumbar spine is analyzed by the finite element method. The mathematical model of the relationship between force and displacement is established by using response surface methodology based on the simulation results. After that, the position control system is constructed based on the mathematical model. Through the simulation of the control system, the trajectory curve of the end of the manipulator is compared and the validity of the mathematical model is verified.