The international journal of medical robotics + computer assisted surgery : MRCAS · 2025
Breakthrough Recognition in Robotic-Assisted UBE Surgery Based on Force Sensing and VAE-MLP.
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Ji Xuquan, Zhang Yonghong, Zhu Yuanyuan, Yang Biao, Hu Lei, Zhao Yu, Liu Wenyong
Аннотация
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.
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.