Autonomous Penetration Perception for Bone Cutting during Laminectomy

In spinal surgery, the surgeon needs superb skills to determine the extent of penetration of cutting tool for preventing the damage of nerves or organs. Thus, a cutting system with autonomous penetration detection would drastically improve the safety and efficiency of surgery. In this study, a hand-hold bone cutting system for laminectomy with on-line autonomous penetration perception of bone is presented. Since the penetration during operation process is invisible, a practical surgeon uses force feedback and sound to perceive cutting state. Inspired by that, the proposed system was designed to recognize different cutting states by measured cutting force and sound signals. A radial basis function neural network was employed to classify different bone cutting states. Results of cross validation prove that the accuracy of perception system reaches up to 95%. The actuation of cutting tool can be automatically stopped as the penetration happens. In addition, the proposed system can also be applied to other bone cutting operations like craniotomy or orthognathic.