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Farshad M, Spirig JM, Suter D, Hoch A, Burkhard MD, Liebmann F, Farshad-Amacker NA, Fürnstahl P. Operator independent reliability of direct augmented reality navigated pedicle screw placement and rod bending. N Am Spine Soc J 2022; 8:100084. [PMID: 35141649 PMCID: PMC8819958 DOI: 10.1016/j.xnsj.2021.100084] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 09/21/2021] [Accepted: 10/02/2021] [Indexed: 12/17/2022]
Abstract
Background AR based navigation of spine surgeries may not only provide accurate surgical execution but also operator independency by compensating for potential skill deficits. “Direct” AR-navigation, namely superposing trajectories on anatomy directly, have not been investigated regarding their accuracy and operator's dependence. Purpose of this study was to prove operator independent reliability and accuracy of both AR assisted pedicle screw navigation and AR assisted rod bending in a cadaver setting. Methods Two experienced spine surgeons and two biomedical engineers (laymen) performed independently from each other pedicle screw instrumentations from L1-L5 in a total of eight lumbar cadaver specimens (20 screws/operator) using a fluoroscopy-free AR based navigation method. Screw fitting rods from L1 to S2-Ala-Ileum were bent bilaterally using an AR based rod bending navigation method (4 rods/operator). Outcome measures were pedicle perforations, accuracy compared to preoperative plan, registration time, navigation time, total rod bending time and operator's satisfaction for these procedures. Results 97.5% of all screws were safely placed (<2 mm perforation), overall mean deviation from planned trajectory was 6.8±3.9°, deviation from planned entry point was 4±2.7 mm, registration time per vertebra was 2:25 min (00:56 to 10:00 min), navigation time per screw was 1:07 min (00:15 to 12:43 min) rod bending time per rod was 4:22 min (02:07 to 10:39 min), operator's satisfaction with AR based screw and rod navigation was 5.38±0.67 (1 to 6, 6 being the best rate). Comparison of surgeons and laymen revealed significant difference in navigation time (1:01 min; 00:15 to 3:00 min vs. 01:37 min; 00:23 to 12:43 min; p = 0.004, respectively) but not in pedicle perforation rate. Conclusions Direct AR based screw and rod navigation using a surface digitization registration technique is reliable and independent of surgical experience. The accuracy of pedicle screw insertion in the lumbar spine is comparable with the current standard techniques.
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Affiliation(s)
- Mazda Farshad
- University Spine Center Zürich, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008 Zurich, Switzerland
| | - José Miguel Spirig
- University Spine Center Zürich, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008 Zurich, Switzerland
| | - Daniel Suter
- University Spine Center Zürich, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008 Zurich, Switzerland.,ROCS: Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland
| | - Armando Hoch
- ROCS: Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland
| | - Marco D Burkhard
- University Spine Center Zürich, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008 Zurich, Switzerland
| | - Florentin Liebmann
- University Spine Center Zürich, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008 Zurich, Switzerland
| | - Nadja A Farshad-Amacker
- Radiology, Balgrist University Hospital, University of Zürich, Forchstrasse 340, 8008 Zürich
| | - Philipp Fürnstahl
- ROCS: Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Forchstrasse 340, 8008, Zurich, Switzerland
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Ayoobi N, Sharifrazi D, Alizadehsani R, Shoeibi A, Gorriz JM, Moosaei H, Khosravi A, Nahavandi S, Gholamzadeh Chofreh A, Goni FA, Klemeš JJ, Mosavi A. Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results Phys 2021; 27:104495. [PMID: 34221854 PMCID: PMC8233414 DOI: 10.1016/j.rinp.2021.104495] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/19/2021] [Accepted: 06/22/2021] [Indexed: 05/17/2023]
Abstract
The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.
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Key Words
- ANFIS, Adaptive Network-based Fuzzy Inference System
- ANN, Artificial Neural Network
- AU, Australia
- Bi-Conv-LSTM, Bidirectional Convolutional Long Short Term Memory
- Bi-GRU, Bidirectional Gated Recurrent Unit
- Bi-LSTM, Bidirectional Long Short-Term Memory
- Bidirectional
- COVID-19 Prediction
- COVID-19, Coronavirus Disease 2019
- Conv-LSTM, Convolutional Long Short Term Memory
- Convolutional Long Short Term Memory (Conv-LSTM)
- DL, Deep Learning
- DLSTM, Delayed Long Short-Term Memory
- Deep learning
- EMRO, Eastern Mediterranean Regional Office
- ES, Exponential Smoothing
- EV, Explained Variance
- GRU, Gated Recurrent Unit
- Gated Recurrent Unit (GRU)
- IR, Iran
- LR, Linear Regression
- LSTM, Long Short-Term Memory
- Lasso, Least Absolute Shrinkage and Selection Operator
- Long Short Term Memory (LSTM)
- MAE, Mean Absolute Error
- MAPE, Mean Absolute Percentage Error
- MERS, Middle East Respiratory Syndrome
- ML, Machine Learning
- MLP-ICA, Multi-layered Perceptron-Imperialist Competitive Calculation
- MSE, Mean Square Error
- MSLE, Mean Squared Log Error
- Machine learning
- New Cases of COVID-19
- New Deaths of COVID-19
- PRISMA, Preferred Reporting Items for Precise Surveys and Meta-Analyses
- RMSE, Root Mean Square Error
- RMSLE, Root Mean Squared Log Error
- RNN, Repetitive Neural Network
- ReLU, Rectified Linear Unit
- SARS, Serious Intense Respiratory Disorder
- SARS-COV, SARS coronavirus
- SARS-COV-2, Serious Intense Respiratory Disorder Coronavirus 2
- SVM, Support Vector Machine
- VAE, Variational Auto Encoder
- WHO, World Health Organization
- WPRO, Western Pacific Regional Office
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Affiliation(s)
- Nooshin Ayoobi
- Department of Mathematics, Savitribai Phule Pune University, Pune 411007, India
| | - Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Spain
| | - Hossein Moosaei
- Department of Mathematics, Faculty of Science, University of Bojnord, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3217, Australia
| | - Abdoulmohammad Gholamzadeh Chofreh
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
| | - Feybi Ariani Goni
- Department of Management, Faculty of Business and Management, Brno University of Technology - VUT Brno, Kolejní 2906/4, 612 00 Brno, Czech Republic
| | - Jiří Jaromír Klemeš
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
- School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
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Liu X, Lin Z. Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory. Energy (Oxf) 2021; 227:120455. [PMID: 36568128 PMCID: PMC9758867 DOI: 10.1016/j.energy.2021.120455] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/12/2021] [Accepted: 03/21/2021] [Indexed: 05/18/2023]
Abstract
Due to lockdown measures taken by the UK government during the Coronavirus disease 2019 pandemic, the national electricity demand profile presented a notably different performance. The Coronavirus disease 2019 crisis has provided a unique opportunity to investigate how such a landscape-scale lockdown can influence the national electricity system. However, the impacts of social and economic restrictions on daily electricity demands are still poorly understood. This paper investigated how the UK-wide electricity demand was influenced during the Coronavirus disease 2019 crisis based on multivariate time series forecasting with Bidirectional Long Short Term Memory, to comprehend its correlations with containment measures, weather conditions, and renewable energy supplies. A deep-learning-based predictive model was established for daily electricity demand time series forecasting, which was trained by multiple features, including the number of coronavirus tests (smoothed), wind speed, ambient temperature, biomass, solar & wind power supplies, and historical electricity demand. Besides, the effects of Coronavirus disease 2019 pandemic on the Net-Zero target of 2050 were also studied through an interlinked approach.
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Key Words
- Adam, Adaptive moment estimation
- Bi-LSTM
- Bi-LSTM, Bidirectional Long Short Term Memory
- CECs, Constant Error Carousels
- COVID-19, Coronavirus disease 2019
- Coronavirus disease 2019
- Electricity demand
- GDP, Gross Domestic Product
- LSTM, Long Short Term Memory
- MSE, Mean Square Error
- MSLE, Mean Squared Logarithmic Error
- RMSE, Root Mean Square Error
- RNN, Recurrent Neural Network
- Renewable power supplies
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Affiliation(s)
- Xiaolei Liu
- James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Zi Lin
- Department of Mechanical & Construction Engineering, Northumbria University, Newcastle Upon Tyne, NE1 8ST, UK
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