Abstract
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.
Highlights
In recent years, there has been an increase in the incidence of spinal diseases represented by lumbar spinal stenosis and lumbar disc herniation
The purpose of this study is to explore the force perception and bone recognition of ultrasonic bone scalpels for robot-assisted vertebral lamina milling based on a backpropagation (BP) neural network to achieve a safe milling strategy
Based on the BP neural network, we developed an algorithmic model suitable for robot-assisted vertebral lamina milling to realize the perception of milling force and recognition of bone layers
Summary
There has been an increase in the incidence of spinal diseases represented by lumbar spinal stenosis and lumbar disc herniation. The vibration amplitude at the end of the drill, frequency and loudness of the milling sound, temperature of the milling parts, and current change of the drill were obtained and monitored to indirectly reflect the mechanical information during bone milling and realize bone tissue recognition [20]–[23] These studies did not consider the different operative methods, speed, and parameter settings of the power equipment, which eventually affect the perception of the milling force. The purpose of this study is to explore the force perception and bone recognition of ultrasonic bone scalpels for robot-assisted vertebral lamina milling based on a backpropagation (BP) neural network to achieve a safe milling strategy.
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