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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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Wang R, Bai H, Xia G, Zhou J, Dai Y, Xue Y. Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy. Eur J Med Res 2023; 28:203. [PMID: 37381061 DOI: 10.1186/s40001-023-01154-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 06/03/2023] [Indexed: 06/30/2023] Open
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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Affiliation(s)
- Rui Wang
- Key Laboratory of Spine and Spinal Cord, Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - He Bai
- Key Laboratory of Spine and Spinal Cord, Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Guangming Xia
- Tianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, 94 Weijin Road, Nankai District, Tianjin, 300071, China
| | - Jiaming Zhou
- Key Laboratory of Spine and Spinal Cord, Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yu Dai
- Tianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, 94 Weijin Road, Nankai District, Tianjin, 300071, China.
| | - Yuan Xue
- Key Laboratory of Spine and Spinal Cord, Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, 300052, 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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Bai H, Wang R, Wang Q, Xia GM, Xue Y, Dai Y, Zhang JX. Motor Bur Milling State Identification via Fast Fourier Transform Analyzing Sound Signal in Cervical Spine Posterior Decompression Surgery. Orthop Surg 2021; 13:2382-2395. [PMID: 34792301 PMCID: PMC8654648 DOI: 10.1111/os.13168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/24/2021] [Accepted: 10/19/2021] [Indexed: 01/18/2023] Open
Abstract
Objectives To investigate the real‐time sensitive feedback parameter of the motor bur milling state in cervical spine posterior decompression surgery, to possibly improve the safety of cervical spine posterior decompression and robot‐assisted spinal surgeries. Methods In this study, the cervical spine of three healthy male and three healthy female pigs were randomly selected. Six porcine cervical spine specimens were fixed to the vibration isolation system. The milling state of the motor bur was defined as the lamina cancellous bone (CA), lamina ventral corticalbone (VCO), and penetrating ventral cortical bone (PVCO). A 5‐mm bur milled the CA and VCO, and a 2‐mm bur milled the VCO and PVCO. A miniature microphone was used to collect the sound signal (SS) of milling lamina which was then extracted using Fast Fourier Transform (FFT). When using 5‐mm and 2‐mm bur to mill, the CA, VCO, and PVCO of each specimen were continuously collected at 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 kHz frequencies for SS magnitudes. The study randomly selected the SS magnitudes of the CA and VCO continuously for 2 s at 1, 2, 3, 4, and 5 kHz frequencies for statistical analyses. When milling the VCO to the PVCO, we randomly collected the SS magnitudes of the VCO for consecutive 2 s and the SS magnitudes of continuous 2 s in the penetrating state at 1, 2, 3, 4, and 5 kHz frequencies for statistical analyses. The independent sample t‐test was used to compare the SS magnitudes of different milling states extracted from the FFT to determine the motor bur milling state. Results The SS magnitudes of the CA and VCO of all specimens extracted from the FFT at 1, 2, and 3 kHz were statistically different (P < 0.01); three specimens were not statistically different at a specific FFT‐extracted frequency (first specimen at 5 kHz, SS magnitudes of the CA were [25.94 ± 8.74] × 10−3, SS magnitudes of the VCO were [28.67 ± 12.94] × 10−3, P = 0.440; second specimen at 4 kHz, SS magnitudes of the CA were [23.79 ± 7.94] × 10−3, SS magnitudes of the VCO were [24.78 ± 4.32] × 10−3, P = 0.629; and third specimen at 5 kHz, SS magnitudes of the CA were [16.76 ± 6.20] × 10−3, SS magnitudes of the VCO were [17.69 ± 6.44] × 10−3, P = 0.643).The SS magnitudes of the VCO and PVCO of all the specimens extracted from the FFT at each frequency were statistically different (P < 0.001). Conclusions Based on the FFT extraction, the SS magnitudes of the motor bur milling state between the CA and VCO, the VCO and PVCO were significantly different, confirming that the SS is a potential sensitive feedback parameter for identifying the motor bur milling state. This study could improve the safety of cervical spine posterior decompression surgery, especially of robot‐assisted surgeries.
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Affiliation(s)
- He Bai
- Department of Orthopaedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Rui Wang
- Department of Orthopaedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Qiu Wang
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
| | - Guang-Ming Xia
- Tianjin Key Laboratory of Intelligent Robotics, College of Computer and Control Engineering, Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China
| | - Yuan Xue
- Department of Orthopaedics Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Yu Dai
- Tianjin Key Laboratory of Intelligent Robotics, College of Computer and Control Engineering, Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China
| | - Jian-Xun Zhang
- Tianjin Key Laboratory of Intelligent Robotics, College of Computer and Control Engineering, Institute of Robotics and Automatic Information System, Nankai University, Tianjin, 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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