Cutting Depth Monitoring Based on Milling Force for Robot-Assisted Laminectomy

Abstract

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. Note to Practitioners - The motivation behind this paper is driven by current safety issues existing in robot-assisted bone-cutting surgery, such as laminectomy. The main contribution of the present work is an algorithm to monitor the current cutting depth. Compared to traditional systems, the key characteristics of the introduced model are: real-time (it can be used intraoperatively); precision and safety (prediction error up to ±0.2 mm in target regions); and ease of use (no other image guidance or tracking system is required). The approach can successfully predict the current milling status and is sensitive to changes in force conditions, which is especially crucial to detect the contact point between the ball-end milling tool and the target bone. In summary, the proposed approach addresses the challenge of improving the safety of robot-assisted surgery via monitoring of the cutting depth. Experimental results on a fresh bovine femur demonstrate promising performances. The present implementation specifically targets bone milling procedures, yet it can easily be extended to a wide range of other similar clinical or industrial applications. Applicability is supported by the fact that the proposed algorithm can be implemented as a plug-in module and integrated into already existing image-guided robotic surgery platforms.