For: | Tiwari A, Poduval M, Bagaria V. Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs. World J Orthop 2022; 13(6): 603-614 [PMID: 35949704 DOI: 10.5312/wjo.v13.i6.603] |
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URL: | https://www.wjgnet.com/2218-5836/full/v13/i6/603.htm |
Number | Citing Articles |
1 |
S. Y. Malathi, Geeta R. Bharamagoudar. A Novel Method Based on CNN-LSTM to Characterize Knee Osteoarthritis from Radiography. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences 2024; 94(2): 423 doi: 10.1007/s40011-023-01545-5
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2 |
Pavan Mahendrakar, Dileep Kumar, Uttam Patil. Comprehensive Study on Scoring and Grading Systems for Predicting the
Severity of Knee Osteoarthritis. Current Rheumatology Reviews 2024; 20(2): 133 doi: 10.2174/0115733971253574231002074759
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3 |
Kamil Kwolek, Dariusz Grzelecki, Konrad Kwolek, Dariusz Marczak, Jacek Kowalczewski, Marcin Tyrakowski. Automated patellar height assessment on high-resolution radiographs with a novel deep learning-based approach. World Journal of Orthopedics 2023; 14(6): 387-398 doi: 10.5312/wjo.v14.i6.387
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4 |
Yun Xin Teoh, Alice Othmani, Siew Li Goh, Juliana Usman, Khin Wee Lai. Predicting Knee Osteoarthritis Pain Severity through A Deep Hybrid Learning Model: Data from the Osteoarthritis Initiative. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2023; : 4148 doi: 10.1109/BIBM58861.2023.10385415
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Vijaya Kishore V, V. Kalpana, G Hemanth Kumar. Evaluating the efficacy of deep learning models for knee osteoarthritis prediction based on Kellgren-Lawrence grading system. e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023; 5: 100266 doi: 10.1016/j.prime.2023.100266
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6 |
S. Sushma, T. Anuradha, D. R. Denslin Brabin, A. Jose Anand. Handbook of Research on Advanced Functional Materials for Orthopedic Applications. Advances in Chemical and Materials Engineering 2023; : 93 doi: 10.4018/978-1-6684-7412-9.ch006
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7 |
Sakshi Dhall, Abhishek Vaish, Raju Vaishya. Machine learning and deep learning for the diagnosis and treatment of ankylosing spondylitis- a scoping review. Journal of Clinical Orthopaedics and Trauma 2024; 52: 102421 doi: 10.1016/j.jcot.2024.102421
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8 |
Manas Ranjan Prusty, Rohit Madhavan Sudharsan, Philip Anand. Enhancing medical image classification with generative AI using latent denoising diffusion probabilistic model and wiener filtering approach. Applied Soft Computing 2024; 161: 111714 doi: 10.1016/j.asoc.2024.111714
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9 |
Effectiveness of Automatic Detection of Osteoarthritis using Convolutional Neural Network (CNN) Method with DenseNet201 on Digital Images of Knee Joint Radiography. E3S Web of Conferences 2023; 448: 02052 doi: 10.1051/e3sconf/202344802052
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10 |
Anjali Tiwari, Amit Kumar Yadav, K.S. Akshay, Vaibhav Bagaria. Evaluation of machine learning models to identify hip arthroplasty implants using transfer learning algorithms. Journal of Clinical Orthopaedics and Trauma 2023; 47: 102312 doi: 10.1016/j.jcot.2023.102312
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11 |
Yun Xin Teoh, Alice Othmani, Khin Wee Lai, Siew Li Goh, Juliana Usman. Stratifying knee osteoarthritis features through multitask deep hybrid learning: Data from the osteoarthritis initiative. Computer Methods and Programs in Biomedicine 2023; 242: 107807 doi: 10.1016/j.cmpb.2023.107807
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12 |
Hassan A. Alshamrani, Mamoon Rashid, Sultan S. Alshamrani, Ali H. D. Alshehri. Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach. Healthcare 2023; 11(9): 1206 doi: 10.3390/healthcare11091206
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13 |
Tejus Surendran, Lisa K. Park, Meagan V. Lauber, Baekdong Cha, Ray S. Jhun, Terence D. Capellini, Deepak Kumar, David T. Felson, Vijaya B. Kolachalama. Survival analysis on subchondral bone length for total knee replacement. Skeletal Radiology 2024; doi: 10.1007/s00256-024-04627-1
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