Breakthrough Recognition in Robotic‐Assisted UBE Surgery Based on Force Sensing and VAE‐MLP

ABSTRACT Background Robotic‐assisted unilateral biportal endoscopic surgery (UBE) is a more accurate and safer technique than traditional open surgical operations. The penetration recognition of ultrasonic drilling remains one of the challenging techniques of robotic‐assisted UBE surgery. Methods We propose a force and VAE‐MLP‐based method for real‐time penetration recognition. During the ultrasonic drilling procedure, the force signals are collected and denoised via Kalman filtering first. The pre‐processed data are then used to extract hidden features and perform classification by Variational Autoencoder (VAE) and Multilayer Perceptron (MLP), respectively, ultimately achieving real‐time penetration recognition. Results Our method achieves superior accuracy (99.32% vs. 95.90%) and faster inference speed (17 vs. 33 ms) compared to the classic time‐series classification algorithm. Robotic ex vivo bone experiments further validated its efficacy. Conclusion The force and VAE‐MLP framework enables fast and accurate penetration detection, which offers a reliable and efficient solution for minimizing nerve damage in UBE surgery.