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With the development of artificial intelligence technologies, spine-surgery robots have gradually been applied in clinical practice, and they have exhibited favorable development prospects. Force perception technology can be used to obtain the milling force, quantify the tactile sensation of a surgeon, and provide feedback or suggestions to the surgeon and robot for safe milling. In this study, a robotic system is proposed to measure the vertebral lamina milling force by using an ultrasonic bone scalpel to realize a safe milling strategy. The developed bone recognition model based on the backpropagation neural network is suitable for robot-assisted vertebral lamina milling using the milling delamination and recognition algorithm analysis. The model uses the characteristic milling force, milling speed, milling depth, and ultrasonic scalpel power as inputs to determine whether milling has reached the inner cortical bone to recognize and judge bone layers. The verification experiment on live animals showed that this model could accurately determine a safe milling endpoint. In general, this recognition model can significantly improve the safety and reliability of robot-assisted laminectomy and has significant translational prospects.

Force Perception and Bone Recognition of Vertebral Lamina Milling by Robot-Assisted Ultrasonic Bone Scalpel Based on Backpropagation Neural Network