Tissue discrimination based on vibratory sense in robot-assisted spine surgery

One of the major challenge in spine surgery is successfully cutting the desired bony structure while avoiding injury to the vital anatomy such as the spinal cord. This paper presents a vibration signal processing method to discriminate different types of tissue in robot-assisted spine surgery. During bone milling process, the tissue vibration signal measured by a laser displacement sensor is decomposed into some frequency sub-bands through the wavelet packet transform, and the harmonic component whose frequency is an integer times of the spindle frequency is obtained. The wavelet energy of the 1^st, 2^nd and 3^rd harmonics is then used as input vector to an artificial neural network for discriminating tissue. To verify the effectiveness of the proposed method, extensive milling experiments are carried out on porcine spines. The experimental results indicate that the method produces up to 100% discrimination accuracy for the vertebra being cut and the spinal cord, and yields the success discrimination rate of more than 80% for the adjacent bony structure and the muscle, so the safety of robot-assisted spine surgery is improved.