State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation

Guan, Fengqing; Sun, Yu; Qi, Xiaozhi; Hu, Ying; Yu, Gang; Zhang, Jianwei

doi:10.3390/s18051484

Cited by 26 publications

(13 citation statements)

References 28 publications

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“…Compared to the approaches from references [6,7,8,9,10,11], the linear oscillation proved to provide further information, especially about the beginning of the drilling process, which clearly indicated the start of the drill channel. Therefore the drill channel length determination became more accurate and robust with the additional information and allowed for the achievement of the presented low measurement uncertainty, without requiring technically complex sound or vibration measurements [12,13].…”

Section: Discussionmentioning

confidence: 99%

“…The vibrations generated during drilling are evaluated with a wavelet analysis with regard to the specific vibration characteristics of cancellous bone and cortical bone. Guan et al 2018 [13] pursued a comparable approach by recording the sound emissions during drilling in a frequency-selective manner and evaluating them together with the feed force and torque using an artificial neural network. For robot-assisted drilling and pedicle milling, Hu et al 2013 [14] used a multiaxis force sensor and a torque sensor to simulate the feeling in a surgeon’s hand when he encounters different bone tissues.…”

Section: Introductionmentioning

confidence: 99%

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Methodical Approach for Determining the Length of Drill Channels in Osteosynthesis

Sorg

Osmers

Fischer

2019

Sensors

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In order to fix a fracture in osteosynthesis, it is necessary to attach screws bicortically to the bone. The length of the screws must be selected correctly in 1-mm increments: otherwise, injury to the surrounding tissue structure or insufficient fixation will result. The drill channel length can only be determined preoperatively to a limited extent and with insufficient accuracy and is therefore determined intraoperatively with a mechanical caliper gauge. This length determination is error-prone, which often leads to a false screw selection and at the same time to considerable complications in the healing process. A novel approach based on a sensory drive train was pursued, with which all mechanical drilling parameters were recorded and evaluated in combination with a length measurement that allows for determining the drill channel length. In order to overcome the limitations of previous drill concepts, a precise length measurement of the drill channel was introduced. The amplitude of a stimulated linear oscillation of the drill was monitored for drilling channel length measurements in order to reliably detect the beginning of the drilling process. The method provides the information required for handheld drilling without the limitation of constant drilling parameters. With initial results from laboratory tests with pig bones, the measurement method for the drill channel length has been validated on a test bench of the drilling machine. With the laboratory tests, a measurement uncertainty of 0.3 mm was achieved, so screws with a 1-mm step width can be reliably selected.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methodical Approach for Determining the Length of Drill Channels in Osteosynthesis

Sorg

Osmers

Fischer

2019

Sensors

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“…In the neural network propagation process, it is necessary to introduce an activation function to adapt the model to nonlinear mapping. When the number of neural network layers is small, a sigmoidal activation function is the optimal choice because it has favorable derivative properties and can map an infinite signal to (0, 1), which is suitable for solving the classification problem in this research [43]. In addition, the convergence error of the neural network was set to 10 -4 , and the learning rate was set to 0.001.…”

Section: B Bone Recognition Algorithm Based On Bp Neural Networkmentioning

confidence: 99%

Force Perception and Bone Recognition of Vertebral Lamina Milling by Robot-Assisted Ultrasonic Bone Scalpel Based on Backpropagation Neural Network

Geng

Chen

et al. 2021

IEEE Access

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With the development of artificial intelligence technologies, spine-surgery robots have gradually been applied in clinical practice, and they have exhibited favorable development prospects. Force perception technology can be used to obtain the milling force, quantify the tactile sensation of a surgeon, and provide feedback or suggestions to the surgeon and robot for safe milling. In this study, a robotic system is proposed to measure the vertebral lamina milling force by using an ultrasonic bone scalpel to realize a safe milling strategy. The developed bone recognition model based on the backpropagation neural network is suitable for robot-assisted vertebral lamina milling using the milling delamination and recognition algorithm analysis. The model uses the characteristic milling force, milling speed, milling depth, and ultrasonic scalpel power as inputs to determine whether milling has reached the inner cortical bone to recognize and judge bone layers. The verification experiment on live animals showed that this model could accurately determine a safe milling endpoint. In general, this recognition model can significantly improve the safety and reliability of robot-assisted laminectomy and has significant translational prospects.

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“…It is a hot research topic to find an objective and accurate method for tissue identification in the spinal surgery area 2 . In addition to traditional imaging methods, tissue recognition techniques based on physical information such as force, acoustics, and bioelectrical impedance are also widely studied 3 , 4 , 5 , 6 , 7 , 8 .…”

Section: Introductionmentioning

confidence: 99%

Tissue Recognition Based on Electrical Impedance Classified by Support Vector Machine in Spinal Operation Area

Chen

Shi²,

et al. 2022

Orthopaedic Surgery

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Objective: One of the major difficulties in spinal surgery is the injury of important tissues caused by tissue misclassification, which is the source of surgical complications. Accurate recognization of the tissues is the key to increase safety and effect as well as to reduce the complications of spinal surgery. The study aimed at tissue recognition in the spinal operation area based on electrical impedance and the boundaries of electrical impedance between cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus.Methods: Two female white swines with body weight of 40 kg were used to expose cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus under general anesthesia and aseptic conditions. The electrical impedance of these tissues at 12 frequencies (in the range of 10-100 kHz) was measured by electrochemical analyzer with a specially designed probe, at 22.0-25.0 C and 50%-60% humidity. Two types of tissue recognition models -one combines principal component analysis (PCA) and support vector machine (SVM) and the other combines combines SVM and ensemble learning -were constructed, and the boundaries of electrical impedance of the five tissues at 12 frequencies of current were figured out. Linear correlation, two-way ANOVA, and paired T-test were conducted to analyze the relationship between the electrical impedance of different tissues at different frequencies. Results:The results suggest that the differences of electrical impedance mainly came from tissue type (p < 0.0001), the electrical impedance of five kinds of tissue was statistically different from each other (p < 0.0001). The tissue recognition accuracy of the algorithm based on principal component analysis and support vector machine ranged from 83%-100%, and the overall accuracy was 95.83%. The classification accuracy of the algorithm based on support vector machine and ensemble learning was 100%, and the boundaries of electrical impedance of five tissues at various frequencies were calculated. Conclusion:The electrical impedance of cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus had significant differences in 10-100 kHz frequency. The application of support vector machine realized the accurate tissue recognition in the spinal operation area based on electrical impedance, which is expected to be translated and applied to tissue recognition during spinal surgery.

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State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation

Cited by 26 publications

References 28 publications

Methodical Approach for Determining the Length of Drill Channels in Osteosynthesis

Methodical Approach for Determining the Length of Drill Channels in Osteosynthesis

Force Perception and Bone Recognition of Vertebral Lamina Milling by Robot-Assisted Ultrasonic Bone Scalpel Based on Backpropagation Neural Network

Tissue Recognition Based on Electrical Impedance Classified by Support Vector Machine in Spinal Operation Area

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