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

Qu, Hao; Geng, Baoduo; Chen, Bingrong; Zhang, Jian; Yang, Yongliang; Hu, Lei; Zhao, Yu

doi:10.1109/access.2021.3069549

Cited by 15 publications

(14 citation statements)

References 36 publications

(33 reference statements)

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“…Robotic bone drilling and milling-related articles used force sensors ( 36 , 37 , 39 , 40 ), load cells ( 41 , 42 ), and electrical current sensors ( 38 ). Wang et al ( 43 ) integrated an optical tracking system in the robotic setup to identify the milling states including cortical, cortical-transit-cancellous, almost-break-cortical, and cancellous states.…”

Section: Resultsmentioning

confidence: 99%

“…Such work formed on the accelerometer (e.g., 47 ), microphone (e.g., 53 ), force sensor (e.g., 37 ), load cell (e.g., 42 ) and laser technologies (e.g., 10 ). Experiments were mainly done with robotic arms (e.g., 38 , 39 ) since the major area of application is the tissue classification during robotic drilling or milling to compensate for the tactile or auditory perception of the human. In addition, ultrasound was added to the orthopedic sensing category, as it can be used to differentiate bone from soft tissues.…”

Section: Discussionmentioning

confidence: 99%

“…In manual bone-cutting procedures, surgeons use their tactile sensation to ensure a safe and accurate operation. Therefore, the majority of studies found in this category (36)(37)(38)(39)(40)(41)(42)(43) proposed systems to compensate for the loss of the surgeon's tactile sensation in robotic surgery with force sensors and load cells (Table 5), whereas (44,45) developed a custom-made tactile sensing probe using balloon expansion and impedance spectroscopy device allowing to differentiate between tissues in brain tumor resection surgery.…”

Section: Clinical Applicationmentioning

confidence: 99%

See 2 more Smart Citations

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

Massalimova

Timmermans²,

Esfandiari³

et al. 2022

Front. Surg.

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Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Clinical Applicationmentioning

confidence: 99%

See 1 more Smart Citation

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

Massalimova

Timmermans²,

Esfandiari³

et al. 2022

Front. Surg.

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show abstract

“…Artificial intelligence algorithms have been widely used in a variety of medical scenarios, including image‐based tissue recognition, bone recognition based on force signals, bone recognition based on sound signals, etc 6,32,33 …”

Section: Discussionmentioning

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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“…The sum coefficient is calculated using the least square method [21], and the cutting force coefficient is a quadratic polynomial. Qu et al show that the cutting force coefficient has little effect on the prediction effect, regardless of whether the edge effect is taken into account or not [22]. Tanaka et al used artificial intelligence technology's direct simulation method to establish the source equation and predict the cutting force [23].…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Design and Optimization of Working Parts of Vertical Spiral Ditching Machine Based on Milling Force and BP Neural Network Model

Chen

2022

Mobile Information Systems

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The existing vertical spiral ditching machine mainly works in flat orchards, and the structural design of ditching components does not match the design of power transmission system, which leads to high ditching power consumption and poor ditching stability. According to the needs of deep application of organic fertilizer in orchards, a spiral cutter was designed to alleviate the common problems of high power consumption and poor stability of ditching machine and reduce the power consumption of ditch cutting. Therefore, the motion parameters and structural parameters of the spiral cutter are optimized. Using ANSYS preprocessor, the finite element model of cutting soil with spiral cutter is established, and the implicit functional relationship between milling force factor and instantaneous cutting thickness is established by using BPNN (BP neural network). On the basis of the established prediction model of milling force, orthogonal experiments were carried out to obtain the best combination of cutter head rotation speed, slotting depth, and advancing speed with minimum power consumption: 20.16 rad/s, 0.3 m, and 0.12 m/s. By investigating the influence of various working parameters on the power consumption of ditching machine, the analysis basis is provided for selecting reasonable working parameters of ditching machine.

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

Cited by 15 publications

References 36 publications

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

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

Design and Optimization of Working Parts of Vertical Spiral Ditching Machine Based on Milling Force and BP Neural Network Model

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