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European Journal of Medical Research
Published in: European Journal of Medical Research 1/2023

Open Access 01-12-2023 | Laminectomy | Research

Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy

Authors: Rui Wang, He Bai, Guangming Xia, Jiaming Zhou, Yu Dai, Yuan Xue

Published in: European Journal of Medical Research | Issue 1/2023

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Abstract

Background

With advances in science and technology, the application of artificial intelligence in medicine has significantly progressed. The purpose of this study is to explore whether the k-nearest neighbors (KNN) machine learning method can identify three milling states based on vibration signals: cancellous bone (CCB), ventral cortical bone (VCB), and penetration (PT) in robot-assisted cervical laminectomy.

Methods

Cervical laminectomies were performed on the cervical segments of eight pigs using a robot. First, the bilateral dorsal cortical bone and part of the CCB were milled with a 5 mm blade and then the bilateral laminae were milled to penetration with a 2 mm blade. During the milling process using the 2 mm blade, the vibration signals were collected by the acceleration sensor, and the harmonic components were extracted using fast Fourier transform. The feature vectors were constructed with vibration signal amplitudes of 0.5, 1.0, and 1.5 kHz and the KNN was then trained by the features vector to predict the milling states.

Results

The amplitudes of the vibration signals between VCB and PT were statistically different at 0.5, 1.0, and 1.5 kHz (P < 0.05), and the amplitudes of the vibration signals between CCB and VCB were significantly different at 0.5 and 1.5 kHz (P < 0.05). The KNN recognition success rates for the CCB, VCB, and PT were 92%, 98%, and 100%, respectively. A total of 6% and 2% of the CCB cases were identified as VCB and PT, respectively; 2% of VCB cases were identified as PT.

Conclusions

The KNN can distinguish different milling states of a high-speed bur in robot-assisted cervical laminectomy based on vibration signals. This method is feasible for improving the safety of posterior cervical decompression surgery.
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Metadata
Title
Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy
Authors
Rui Wang
He Bai
Guangming Xia
Jiaming Zhou
Yu Dai
Yuan Xue
Publication date
01-12-2023
Publisher
BioMed Central
Published in
European Journal of Medical Research / Issue 1/2023
Electronic ISSN: 2047-783X
DOI
https://doi.org/10.1186/s40001-023-01154-y

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