背景:随着科学技术的进步,人工智能在医学中的应用取得了显著进展。本研究的目的是探讨k近邻(KNN)机器学习方法是否可以根据振动信号识别三种铣削状态:松质骨(CCB),腹侧皮质骨(VCB),机器人辅助颈椎椎板切除术中的穿透性(PT)。
方法:使用机器人对八只猪的子宫颈段进行子宫颈椎板切除术。首先,双侧背侧皮质骨和部分CCB用5mm刀片研磨,然后双侧椎板用2mm刀片研磨穿透。在使用2毫米刀片的铣削过程中,振动信号由加速度传感器采集,并利用快速傅里叶变换提取谐波分量。以0.5、1.0和1.5kHz的振动信号振幅构建特征向量,然后通过特征向量训练KNN以预测铣削状态。
结果:在0.5、1.0和1.5kHz时,VCB和PT之间的振动信号幅度有统计学差异(P<0.05),在0.5和1.5kHz时,CCB和VCB之间的振动信号振幅差异有统计学意义(P<0.05)。CCB的KNN识别成功率,VCB,PT为92%,98%,100%,分别。共有6%和2%的CCB病例被确定为VCB和PT,分别为2%的VCB病例被确定为PT。
结论:KNN可以根据振动信号区分机器人辅助颈椎椎板切除术中高速钻头的不同铣削状态。该方法对于提高颈椎后路减压手术的安全性是可行的。
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.