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Virtual and Real Bidirectional Driving System for the Synchronization of Manipulations in Robotic Joint Surgeries. MACHINES 2022. [DOI: 10.3390/machines10070530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Surgical robots are increasingly important in orthopedic surgeries to assist or replace surgeons in completing operations. During joint surgeries, the patient’s joint needs to be adjusted several times by the surgeon. Therefore, the virtual model, built on the preoperative medical images, cannot match the actual variation of the patient’s joint during the surgery. Conventional virtual reality techniques cannot fully satisfy the requirements of the joint surgeries. This paper proposes a real and virtual bidirectional driving method to synchronize the manipulations in both the real operation site and the virtual scene. The dynamic digital twin of the patient’s joint is obtained by decoupling the joint and dynamically updating its pose via the intraoperative measurements. During surgery, the surgeon can intuitively monitor the real-time position of the patient and the surgical tool through the system and can also manipulate the surgical robot in the virtual scene. In addition, the system can provide visual guidance to the surgeon when the patient’s joint is adjusted. A prototype system is developed for orthopedic surgeries. Proof-of-concept joint surgery demo is carried out to verify the effectiveness of the proposed method. Experimental results show that the proposed system can synchronize the manipulations in both the real operation site and the virtual scene, thus realizing the bidirectional driving.
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Wårdell K, Nordin T, Vogel D, Zsigmond P, Westin CF, Hariz M, Hemm S. Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization. Front Neurosci 2022; 16:834026. [PMID: 35478842 PMCID: PMC9036439 DOI: 10.3389/fnins.2022.834026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 03/01/2022] [Indexed: 01/10/2023] Open
Abstract
Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.
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Affiliation(s)
- Karin Wårdell
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Teresa Nordin
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Dorian Vogel
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Peter Zsigmond
- Department of Neurosurgery and Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Carl-Fredrik Westin
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Marwan Hariz
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, United Kingdom
- Department of Clinical Sciences, Neuroscience, Ume University, Umeå, Sweden
| | - Simone Hemm
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
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Results of a French pilot database of standard of care of chronic subdural hematoma. Neurochirurgie 2022; 68:409-413. [DOI: 10.1016/j.neuchi.2022.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 11/18/2022]
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Peralta M, Jannin P, Baxter JSH. Machine learning in deep brain stimulation: A systematic review. Artif Intell Med 2021; 122:102198. [PMID: 34823832 DOI: 10.1016/j.artmed.2021.102198] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 09/23/2021] [Accepted: 10/12/2021] [Indexed: 11/16/2022]
Abstract
Deep Brain Stimulation (DBS) is an increasingly common therapy for a large range of neurological disorders, such as abnormal movement disorders. The effectiveness of DBS in terms of controlling patient symptomatology has made this procedure increasingly used over the past few decades. Concurrently, the popularity of Machine Learning (ML), a subfield of artificial intelligence, has skyrocketed and its influence has more recently extended to medical domains such as neurosurgery. Despite its growing research interest, there has yet to be a literature review specifically on the use of ML in DBS. We have followed a fully systematic methodology to obtain a corpus of 73 papers. In each paper, we identified the clinical application, the type/amount of data used, the method employed, and the validation strategy, further decomposed into 12 different sub-categories. The papers overall illustrated some existing trends in how ML is used in the context of DBS, including the breath of the problem domain and evolving techniques, as well as common frameworks and limitations. This systematic review analyzes at a broad level how ML have been recently used to address clinical problems on DBS, giving insight into how these new computational methods are helping to push the state-of-the-art of functional neurosurgery. DBS clinical workflow is complex, involves many specialists, and raises several clinical issues which have partly been addressed with artificial intelligence. However, several areas remain and those that have been recently addressed with ML are by no means considered "solved" by the community nor are they closed to new and evolving methods.
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Affiliation(s)
- Maxime Peralta
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - Pierre Jannin
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - John S H Baxter
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France.
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Park KH, Sun S, Lim YH, Park HR, Lee JM, Park K, Jeon B, Park HP, Kim HC, Paek SH. Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease. PLoS One 2021; 16:e0244133. [PMID: 33497391 PMCID: PMC7837468 DOI: 10.1371/journal.pone.0244133] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 12/03/2020] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clinical outcomes. OBJECTIVE We applied deep learning techniques to microelectrode recording (MER) signals to better predict motor function improvement, represented by the UPDRS part III scores, after bilateral STN DBS in patients with advanced PD. If we find the optimal stimulation point with MER by deep learning, we can improve the clinical outcome of STN DBS even under restrictions such as general anesthesia or non-cooperation of the patients. METHODS In total, 696 4-second left-side MER segments from 34 patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included. We transformed the original signal into three wavelets of 1-50 Hz, 50-500 Hz, and 500-5,000 Hz. The wavelet-transformed MER was used for input data of the deep learning. The patients were divided into two groups, good response and moderate response groups, according to DBS on to off ratio of UPDRS part III score for the off-medication state, 6 months postoperatively. The ratio were used for output data in deep learning. The Visual Geometry Group (VGG)-16 model with a multitask learning algorithm was used to estimate the bilateral effect of DBS. Different ratios of the loss function in the task-specific layer were applied considering that DBS affects both sides differently. RESULTS When we divided the MER signals according to the frequency, the maximal accuracy was higher in the 50-500 Hz group than in the 1-50 Hz and 500-5,000 Hz groups. In addition, when the multitask learning method was applied, the stability of the model was improved in comparison with single task learning. The maximal accuracy (80.21%) occurred when the right-to-left loss ratio was 5:1 or 6:1. The area under the curve (AUC) was 0.88 in the receiver operating characteristic (ROC) curve. CONCLUSION Clinical improvements in PD patients who underwent bilateral STN DBS could be predicted based on a multitask deep learning-based MER analysis.
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Affiliation(s)
- Kwang Hyon Park
- Department of Neurosurgery, Seoul National University Hospital, Seoul, Korea
| | - Sukkyu Sun
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
| | - Yong Hoon Lim
- Department of Neurosurgery, Seoul National University Hospital, Seoul, Korea
| | - Hye Ran Park
- Department of Neurosurgery, Soonchunhyang University Seoul Hospital, Seoul, Korea
| | - Jae Meen Lee
- Department of Neurosurgery, Pusan National University Hospital, Busan, Korea
| | - Kawngwoo Park
- Department of Neurosurgery, Gachon University Gil Medical Center, Incheon, Korea
| | - Beomseok Jeon
- Department of Neurology, Seoul National University Hospital, Seoul, Korea
| | - Hee-Pyoung Park
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Hee Chan Kim
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
- Department of Biomedical Engineering College of Medicine, Seoul National University, Seoul, Korea
- Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea
| | - Sun Ha Paek
- Department of Neurosurgery, Seoul National University Hospital, Seoul, Korea
- Ischemia Hypoxia Disease Institute, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
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Peña E, Zhang S, Patriat R, Aman JE, Vitek JL, Harel N, Johnson MD. Multi-objective particle swarm optimization for postoperative deep brain stimulation targeting of subthalamic nucleus pathways. J Neural Eng 2018; 15:066020. [PMID: 30211697 PMCID: PMC6424118 DOI: 10.1088/1741-2552/aae12f] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE The effectiveness of deep brain stimulation (DBS) therapy strongly depends on precise surgical targeting of intracranial leads and on clinical optimization of stimulation settings. Recent advances in surgical targeting, multi-electrode designs, and multi-channel independent current-controlled stimulation are poised to enable finer control in modulating pathways within the brain. However, the large stimulation parameter space enabled by these technologies also poses significant challenges for efficiently identifying the most therapeutic DBS setting for a given patient. Here, we present a computational approach for programming directional DBS leads that is based on a non-convex optimization framework for neural pathway targeting. APPROACH The algorithm integrates patient-specific pre-operative 7 T MR imaging, post-operative CT scans, and multi-objective particle swarm optimization (MOPSO) methods using dominance based-criteria and incorporating multiple neural pathways simultaneously. The algorithm was evaluated on eight patient-specific models of subthalamic nucleus (STN) DBS to identify electrode configurations and stimulation amplitudes to optimally activate or avoid six clinically relevant pathways: motor territory of STN, non-motor territory of STN, internal capsule, superior cerebellar peduncle, thalamic fasciculus, and hyperdirect pathway. MAIN RESULTS Across the patient-specific models, single-electrode stimulation showed significant correlations across modeled pathways, particularly for motor and non-motor STN efferents. The MOPSO approach was able to identify multi-electrode configurations that achieved improved targeting of motor STN efferents and hyperdirect pathway afferents than that achieved by any single-electrode monopolar setting at equivalent power levels. SIGNIFICANCE These results suggest that pathway targeting with patient-specific model-based optimization algorithms can efficiently identify non-trivial electrode configurations for enhancing activation of clinically relevant pathways. However, the results also indicate that inter-pathway correlations can limit selectivity for certain pathways even with directional DBS leads.
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Affiliation(s)
- Edgar Peña
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Simeng Zhang
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Remi Patriat
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, United States
| | - Joshua E. Aman
- Department of Neurology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, United States
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
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Shah AA, Alonso F, Vogel D, Wardell K, Coste J, Lemaire JJ, Pison D, Hemm S. Analysis of adverse effects of stimulation during DBS surgery by patient-specific FEM simulations. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:2222-2225. [PMID: 30440847 DOI: 10.1109/embc.2018.8512796] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Deep brain stimulation (DBS) represents today a well-established treatment for movement disorders. Nevertheless the exact mechanism of action of DBS remains incompletely known. During surgery, numerous stimulation tests are frequently performed in order to evaluate therapeutic and adverse effects before choosing the optimal implantation site for the DBS lead. Anatomical structures responsible for the induced adverse effects have been investigated previously, but only based on stimulation data obtained with the implanted DBS lead. The present study introduces a methodology to identify these anatomical structures during intraoperative stimulation tests based on patient-specific electric field simulations and visualization on the patient specific anatomy. The application to 4 patients undergoing DBS surgery and presenting dysarthria, paresthesia or pyramidal effects shows the different anatomical structures, which might be responsible for the adverse effects. Several of the identified structures have been previously described in the literature. To draw any statistically significant conclusions, the methodology has to be applied to further patients. Together with the visualization of the therapeutic effects, this new approach could assist the neurosurgeons in the future in choosing the optimal implant position.
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