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Massalimova A, Timmermans M, Cavalcanti N, Suter D, Seibold M, Carrillo F, Laux CJ, Sutter R, Farshad M, Denis K, Fürnstahl P. Automatic breach detection during spine pedicle drilling based on vibroacoustic sensing. Artif Intell Med 2023; 144:102641. [PMID: 37783536 DOI: 10.1016/j.artmed.2023.102641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 10/04/2023]
Abstract
Pedicle drilling is a complex and critical spinal surgery task. Detecting breach or penetration of the surgical tool to the cortical wall during pilot-hole drilling is essential to avoid damage to vital anatomical structures adjacent to the pedicle, such as the spinal cord, blood vessels, and nerves. Currently, the guidance of pedicle drilling is done using image-guided methods that are radiation intensive and limited to the preoperative information. This work proposes a new radiation-free breach detection algorithm leveraging a non-visual sensor setup in combination with deep learning approach. Multiple vibroacoustic sensors, such as a contact microphone, a free-field microphone, a tri-axial accelerometer, a uni-axial accelerometer, and an optical tracking system were integrated into the setup. Data were collected on four cadaveric human spines, ranging from L5 to T10. An experienced spine surgeon drilled the pedicles relying on optical navigation. A new automatic labeling method based on the tracking data was introduced. Labeled data was subsequently fed to the network in mel-spectrograms, classifying the data into breach and non-breach. Different sensor types, sensor positioning, and their combinations were evaluated. The best results in breach recall for individual sensors could be achieved using contact microphones attached to the dorsal skin (85.8%) and uni-axial accelerometers clamped to the spinous process of the drilled vertebra (81.0%). The best-performing data fusion model combined the latter two sensors with a breach recall of 98%. The proposed method shows the great potential of non-visual sensor fusion for avoiding screw misplacement and accidental bone breaches during pedicle drilling and could be extended to further surgical applications.
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Affiliation(s)
- Aidana Massalimova
- Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Zurich, 8008, Switzerland.
| | - Maikel Timmermans
- KU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation Group, Leuven, 3001, Belgium.
| | - Nicola Cavalcanti
- Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Zurich, 8008, Switzerland
| | - Daniel Suter
- Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Zurich, 8008, Switzerland
| | - Matthias Seibold
- Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Zurich, 8008, Switzerland
| | - Fabio Carrillo
- Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Zurich, 8008, Switzerland
| | - Christoph J Laux
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, 8008, Switzerland
| | - Reto Sutter
- Department of Radiology, Balgrist University Hospital, Zurich, 8008, Switzerland
| | - Mazda Farshad
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, 8008, Switzerland
| | - Kathleen Denis
- KU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation Group, Leuven, 3001, Belgium
| | - Philipp Fürnstahl
- Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Zurich, 8008, Switzerland
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Timmermans M, Massalimova A, Li R, Davoodi A, Goossens Q, Niu K, Vander Poorten E, Fürnstahl P, Denis K. State-of-the-Art of Non-Radiative, Non-Visual Spine Sensing with a Focus on Sensing Forces, Vibrations and Bioelectrical Properties: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:8094. [PMID: 37836924 PMCID: PMC10574884 DOI: 10.3390/s23198094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 10/15/2023]
Abstract
In the research field of robotic spine surgery, there is a big upcoming momentum for surgeon-like autonomous behaviour and surgical accuracy in robotics which goes beyond the standard engineering notions such as geometric precision. The objective of this review is to present an overview of the state of the art in non-visual, non-radiative spine sensing for the enhancement of surgical techniques in robotic automation. It provides a vantage point that facilitates experimentation and guides new research projects to what has not been investigated or integrated in surgical robotics. Studies were identified, selected and processed according to the PRISMA guidelines. Relevant study characteristics that were searched for include the sensor type and measured feature, the surgical action, the tested sample, the method for data analysis and the system's accuracy of state identification. The 6DOF f/t sensor, the microphone and the electromyography probe were the most commonly used sensors in each category, respectively. The performance of the electromyography probe is unsatisfactory in terms of preventing nerve damage as it can only signal after the nerve is disturbed. Feature thresholding and artificial neural networks were the most common decision algorithms for state identification. The fusion of different sensor data in the decision algorithm improved the accuracy of state identification.
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Affiliation(s)
- Maikel Timmermans
- KU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation, 3000 Leuven, Belgium; (Q.G.); (K.D.)
| | - Aidana Massalimova
- Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, 8008 Zurich, Switzerland; (A.M.); (P.F.)
| | - Ruixuan Li
- KU Leuven, Department of Mechanical Engineering, Robot-Assisted Surgery Group (RAS), 3000 Leuven, Belgium; (R.L.); (A.D.); (K.N.); (E.V.P.)
| | - Ayoob Davoodi
- KU Leuven, Department of Mechanical Engineering, Robot-Assisted Surgery Group (RAS), 3000 Leuven, Belgium; (R.L.); (A.D.); (K.N.); (E.V.P.)
| | - Quentin Goossens
- KU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation, 3000 Leuven, Belgium; (Q.G.); (K.D.)
| | - Kenan Niu
- KU Leuven, Department of Mechanical Engineering, Robot-Assisted Surgery Group (RAS), 3000 Leuven, Belgium; (R.L.); (A.D.); (K.N.); (E.V.P.)
| | - Emmanuel Vander Poorten
- KU Leuven, Department of Mechanical Engineering, Robot-Assisted Surgery Group (RAS), 3000 Leuven, Belgium; (R.L.); (A.D.); (K.N.); (E.V.P.)
| | - Philipp Fürnstahl
- Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, 8008 Zurich, Switzerland; (A.M.); (P.F.)
| | - Kathleen Denis
- KU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation, 3000 Leuven, Belgium; (Q.G.); (K.D.)
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Li S, Zhong X, Yang Y, Qi X, Hu Y, Yang X. Force-Position Hybrid Compensation Control for Path Deviation in Robot-Assisted Bone Drilling. SENSORS (BASEL, SWITZERLAND) 2023; 23:7307. [PMID: 37631841 PMCID: PMC10458884 DOI: 10.3390/s23167307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/09/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023]
Abstract
Bone drilling is a common procedure in orthopedic surgery and is frequently attempted using robot-assisted techniques. However, drilling on rigid, slippery, and steep cortical surfaces, which are frequently encountered in robot-assisted operations due to limited workspace, can lead to tool path deviation. Path deviation can have significant impacts on positioning accuracy, hole quality, and surgical safety. In this paper, we consider the deformation of the tool and the robot as the main factors contributing to path deviation. To address this issue, we establish a multi-stage mechanistic model of tool-bone interaction and develop a stiffness model of the robot. Additionally, a joint stiffness identification method is proposed. To compensate for path deviation in robot-assisted bone drilling, a force-position hybrid compensation control framework is proposed based on the derived models and a compensation strategy of path prediction. Our experimental results validate the effectiveness of the proposed compensation control method. Specifically, the path deviation is significantly reduced by 56.6%, the force of the tool is reduced by 38.5%, and the hole quality is substantially improved. The proposed compensation control method based on a multi-stage mechanistic model and joint stiffness identification method can significantly improve the accuracy and safety of robot-assisted bone drilling.
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Affiliation(s)
- Shibo Li
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (S.L.); (X.Z.); (Y.Y.); (X.Q.); (Y.H.)
| | - Xin Zhong
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (S.L.); (X.Z.); (Y.Y.); (X.Q.); (Y.H.)
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China
| | - Yuanyuan Yang
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (S.L.); (X.Z.); (Y.Y.); (X.Q.); (Y.H.)
| | - Xiaozhi Qi
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (S.L.); (X.Z.); (Y.Y.); (X.Q.); (Y.H.)
| | - Ying Hu
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (S.L.); (X.Z.); (Y.Y.); (X.Q.); (Y.H.)
| | - Xiaojun Yang
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China
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Chen B, Shi Y, Li J, Zhai J, Liu L, Liu W, Hu L, Zhao Y. Tissue Recognition Based on Electrical Impedance Classified by Support Vector Machine in Spinal Operation Area. Orthop Surg 2022; 14:2276-2285. [PMID: 35913262 PMCID: PMC9483044 DOI: 10.1111/os.13406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/24/2022] [Accepted: 06/25/2022] [Indexed: 11/26/2022] Open
Abstract
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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Affiliation(s)
- Bingrong Chen
- Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongwang Shi
- MD Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jiahao Li
- Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiliang Zhai
- Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liang Liu
- China Astronaut Research and Training Center, Beijing, China
| | - Wenyong Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Lei Hu
- School of Mechanical Engineering and Automation, Beihang University, Beijing, China
| | - Yu Zhao
- Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Lang Z, Wang Q, Wu X, Liu Y, He D, Fan M, Shi Z, Tian W. Drilling Speed and Bone Temperature of a Robot-assisted Ultrasonic Osteotome Applied to Vertebral Cancellous Bone. Spine (Phila Pa 1976) 2021; 46:E760-E768. [PMID: 33394989 DOI: 10.1097/brs.0000000000003902] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN An experimental investigation of a robot-assisted ultrasonic osteotome applied to vertebral cancellous bone. OBJECTIVE The aim of this study was to investigate the effect of various ultrasonic parameter settings on temperature in the drilling site and penetration time and determine the most suitable parameters for efficient and safe robot-based ultrasonically assisted bone drilling in spinal surgery. SUMMARY OF BACKGROUND DATA A robot-assisted ultrasonic osteotome device may be safe and effective for spinal drilling. METHODS Sixty specimens of bovine vertebral cancellous were randomly assigned to one of six groups, which varied by mode of ultrasonic vibration (L-T and L) and feed rate (one percent [0.8 mm/s], two percent [1.6 mm/s], and three pecent [2.4 mm/s]). Maximum temperature in the drilling site and penetration time was recorded. RESULTS Maximum temperature in the drilling site decreased as output power increased for L-T and L modes, was significantly lower for L-T compared to L mode at each feed rate and power setting, was significantly different at feed rates of 1.6 mm/s versus 0.8 mm/s and 2.4 mm/s versus 0.8 mm/s for L-T mode at an output power of 60 W and 84 W, but was not influenced by feed rate for L mode. Penetration time did not significantly improve as output power increased for both L-T and L modes, was significantly decreased with increased feed rates, but was not significantly different between L-T and L modes. CONCLUSION The optimal parameters for applying a robot-assisted ultrasonic osteotome to vertebral cancellous bone are L-T mode, maximum output power of 120 W, and maximum feed rate of 2.4 mm/s.Level of Evidence: 4.
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Affiliation(s)
- Zhao Lang
- Department of Spine Surgery, Beijing Ji Shui Tan Hospital, Beijing, China
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Qu H, Zhao Y. Advances in tissue state recognition in spinal surgery: a review. Front Med 2021; 15:575-584. [PMID: 33990898 DOI: 10.1007/s11684-020-0816-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 07/27/2020] [Indexed: 12/27/2022]
Abstract
Spinal disease is an important cause of cervical discomfort, low back pain, radiating pain in the limbs, and neurogenic intermittent claudication, and its incidence is increasing annually. From the etiological viewpoint, these symptoms are directly caused by the compression of the spinal cord, nerve roots, and blood vessels and are most effectively treated with surgery. Spinal surgeries are primarily performed using two different techniques: spinal canal decompression and internal fixation. In the past, tactile sensation was the primary method used by surgeons to understand the state of the tissue within the operating area. However, this method has several disadvantages because of its subjectivity. Therefore, it has become the focus of spinal surgery research so as to strengthen the objectivity of tissue state recognition, improve the accuracy of safe area location, and avoid surgical injury to tissues. Aside from traditional imaging methods, surgical sensing techniques based on force, bioelectrical impedance, and other methods have been gradually developed and tested in the clinical setting. This article reviews the progress of different tissue state recognition methods in spinal surgery and summarizes their advantages and disadvantages.
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Affiliation(s)
- Hao Qu
- Department of Orthopaedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Yu Zhao
- Department of Orthopaedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
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Seibold M, Maurer S, Hoch A, Zingg P, Farshad M, Navab N, Fürnstahl P. Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery. Sci Rep 2021; 11:3993. [PMID: 33597615 PMCID: PMC7889943 DOI: 10.1038/s41598-021-83506-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 02/03/2021] [Indexed: 11/24/2022] Open
Abstract
In this work, we developed and validated a computer method capable of robustly detecting drill breakthrough events and show the potential of deep learning-based acoustic sensing for surgical error prevention. Bone drilling is an essential part of orthopedic surgery and has a high risk of injuring vital structures when over-drilling into adjacent soft tissue. We acquired a dataset consisting of structure-borne audio recordings of drill breakthrough sequences with custom piezo contact microphones in an experimental setup using six human cadaveric hip specimens. In the following step, we developed a deep learning-based method for the automated detection of drill breakthrough events in a fast and accurate fashion. We evaluated the proposed network regarding breakthrough detection sensitivity and latency. The best performing variant yields a sensitivity of \documentclass[12pt]{minimal}
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\begin{document}$${\hbox { ms}}$$\end{document}ms. The validation and performance evaluation of our solution demonstrates promising results for surgical error prevention by automated acoustic-based drill breakthrough detection in a realistic experiment while being multiple times faster than a surgeon’s reaction time. Furthermore, our proposed method represents an important step for the translation of acoustic-based breakthrough detection towards surgical use.
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Affiliation(s)
- Matthias Seibold
- Computer Aided Medical Procedures (CAMP), Technical University of Munich, 85748, Munich, Germany. .,Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Balgrist Campus, 8008, Zurich, Switzerland.
| | - Steven Maurer
- Balgrist University Hospital, 8008, Zurich, Switzerland
| | - Armando Hoch
- Balgrist University Hospital, 8008, Zurich, Switzerland
| | - Patrick Zingg
- Balgrist University Hospital, 8008, Zurich, Switzerland
| | - Mazda Farshad
- Balgrist University Hospital, 8008, Zurich, Switzerland
| | - Nassir Navab
- Computer Aided Medical Procedures (CAMP), Technical University of Munich, 85748, Munich, Germany
| | - Philipp Fürnstahl
- Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Balgrist Campus, 8008, Zurich, Switzerland.,Balgrist University Hospital, 8008, Zurich, Switzerland
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Sun Y, Wang L, Jiang Z, Li B, Hu Y, Tian W. State recognition of decompressive laminectomy with multiple information in robot-assisted surgery. Artif Intell Med 2019; 102:101763. [PMID: 31980100 DOI: 10.1016/j.artmed.2019.101763] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 11/04/2019] [Accepted: 11/10/2019] [Indexed: 10/25/2022]
Abstract
The decompressive laminectomy is a common operation for treatment of lumbar spinal stenosis. The tools for grinding and drilling are used for fenestration and internal fixation, respectively. The state recognition is one of the main technologies in robot-assisted surgery, especially in tele-surgery, because surgeons have limited perception during remote-controlled robot-assisted surgery. The novelty of this paper is that a state recognition system is proposed for the robot-assisted tele-surgery. By combining the learning methods and traditional methods, the robot from the slave-end can think about the current operation state like a surgeon, and provide more information and decision suggestions to the master-end surgeon, which aids surgeons work safer in tele-surgery. For the fenestration, we propose an image-based state recognition method that consists a U-Net derived network, grayscale redistribution and dynamic receptive field assisting in controlling the grinding process to prevent the grinding-bit from crossing the inner edge of the lamina to damage the spinal nerves. For the internal fixation, we propose an audio and force-based state recognition method that consists signal features extraction methods, LSTM-based prediction and information fusion assisting in monitoring the drilling process to prevent the drilling-bit from crossing the outer edge of the vertebral pedicle to damage the spinal nerves. Several experiments are conducted to show the reliability of the proposed system in robot-assisted surgery.
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Affiliation(s)
- Yu Sun
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen, 518055, China; Harbin Institute of Technology (Shenzhen), University Town of Shenzhen, Shenzhen, 518055, China.
| | - Li Wang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen, 518055, China.
| | | | - Bing Li
- Harbin Institute of Technology (Shenzhen), University Town of Shenzhen, Shenzhen, 518055, China.
| | - Ying Hu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen, 518055, China; SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518055, China.
| | - Wei Tian
- Beijing Jishuitan Hospital, Beijing, 100035, China.
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Sorg M, Osmers J, Fischer A. Methodical Approach for Determining the Length of Drill Channels in Osteosynthesis. SENSORS 2019; 19:s19163532. [PMID: 31412550 PMCID: PMC6720950 DOI: 10.3390/s19163532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/05/2019] [Accepted: 08/09/2019] [Indexed: 12/02/2022]
Abstract
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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Affiliation(s)
- Michael Sorg
- Bremen Institute for Metrology, Automation and Quality Science, University of Bremen, 28359 Bremen, Germany.
| | - Jan Osmers
- Bremen Institute for Metrology, Automation and Quality Science, University of Bremen, 28359 Bremen, Germany
| | - Andreas Fischer
- Bremen Institute for Metrology, Automation and Quality Science, University of Bremen, 28359 Bremen, Germany
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