A Surgical State Identifying Method based on BiLSTM with Vibration Processing for Improving Safety of Bone Milling System*

Abstract

In spinal surgery, ensuring surgical precision and safety is paramount. Traditionally, surgeons have relied on their experience to determine when to cease milling as the cutter approaches the spinal cord; However, improper technique during this process can lead to complications, such as vertebral plate fractures and spinal cord injuries. This paper investigates the development of a robot capable of high-precision recognition of the milling state. Initially, we identify vibration signals as the basis for state recognition, establishing their feasibility through theoretical analysis, which provides a foundation for the creation of datasets for subsequent milling experiments. We then conducted milling experiments using pig scapulae and designed neural networks for state identification. Vibration signals corresponding to varying milling depth and the proportion of cortical and cancellous bone layers were collected. A BiLSTM-based neural network was developed to identify the milling depth and the proportion of the bone layers, achieving the desired outcomes within an acceptable error range.The results demonstrate that the proposed system achieves high accuracy in state recognition, with errors falling within an acceptable range. This research highlights the potential of integrating advanced neural networks and vibration analysis into robotic systems to enhance precision and safety in spinal surgery.