Safety control strategy of spinal lamina cutting based on force and cutting depth signals

Abstract

Abstract Laminectomy is one of the most common posterior spinal operations. Since the lamina is adjacent to important tissues such as nerves, once damaged, it can cause serious complications and even lead to paralysis. In order to prevent the above injuries and complications, ultrasonic bone scalpel and surgical robots have been introduced into spinal laminectomy, and many scholars have studied the recognition method of the bone tissue status. Currently, almost all methods to achieve recognition of bone tissue are based on sensor signals collected by high‐precision sensors installed at the end of surgical robots. However, the previous methods could not accurately identify the state of spinal bone tissue. Innovatively, the identification of bone tissue status was regarded as a time series classification task, and the classification algorithm LSTM‐FCN was used to process fusion signals composed of force and cutting depth signals, thus achieving an accurate classification of the lamina bone tissue status. In addition, it was verified that the accuracy of the proposed method could reach 98.85% in identifying the state of porcine spinal laminectomy. And the maximum penetration distance can be controlled within 0.6 mm, which is safe and can be used in practice.

Keywords

• artificial neural network
• intelligent robots
• robotics
• surgery