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Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks [article]

Joseph Antony, Kevin McGuinness, Kieran Moran, Noel E O'Connor
<span title="2017-03-29">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN).  ...  We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss  ...  Acknowledgment This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under grant numbers SFI/12/RC/2289 and 15/SIRG/3283.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1703.09856v1">arXiv:1703.09856v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vpfxhmewwjcbjovq4uzugyfhfu">fatcat:vpfxhmewwjcbjovq4uzugyfhfu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930115053/https://arxiv.org/pdf/1703.09856v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/23/3b/233b89fca5829f38e1528ea5c0bb5591873b5e96.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1703.09856v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity Using Convolutional Neural Networks [chapter]

Joseph Antony, Kevin McGuinness, Kieran Moran, Noel E. O'Connor
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN).  ...  We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss  ...  Acknowledgment This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under grant numbers SFI/12/RC/2289 and 15/SIRG/3283.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-62416-7_27">doi:10.1007/978-3-319-62416-7_27</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zcaglycv7jcilp3kngldcycfuy">fatcat:zcaglycv7jcilp3kngldcycfuy</a> </span>
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Assessing Knee OA Severity with CNN attention-based end-to-end architectures [article]

Marc Górriz, Joseph Antony, Kevin McGuinness, Xavier Giró-i-Nieto, Noel E. O'Connor
<span title="2019-08-23">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable  ...  (OAI) and multicenter osteoarthritis study (MOST).  ...  This manuscript was prepared using MOST data and does not necessarily reflect the opinions or views of MOST investigators.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1908.08856v1">arXiv:1908.08856v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gm2kzia5yrhzrf2ztsnzj75cwu">fatcat:gm2kzia5yrhzrf2ztsnzj75cwu</a> </span>
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Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity [article]

Joseph Antony, Kevin McGuinness, Kieran Moran, Noel E O' Connor
<span title="2019-08-23">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the associated clinical and  ...  Next, three new approaches are introduced: 1) to automatically detect the knee joint region using a fully convolutional network, 2) to automatically assess the radiographic knee OA using CNNs trained from  ...  This manuscript was prepared using MOST data and does not necessarily reflect the opinions or views of MOST investigators.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1908.08840v1">arXiv:1908.08840v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/j7i6fwsnkrc5vguemlpv3d5tra">fatcat:j7i6fwsnkrc5vguemlpv3d5tra</a> </span>
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Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network [article]

Rohit Kumar Jain, Prasen Kumar Sharma, Sibaji Gaj, Arijit Sur, Palash Ghosh
<span title="2021-06-27">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Knee Osteoarthritis (OA) is a destructive joint disease identified by joint stiffness, pain, and functional disability concerning millions of lives across the globe.  ...  This paper presents a deep learning-based framework, namely OsteoHRNet, that automatically assesses the Knee OA severity in terms of Kellgren and Lawrence (KL) grade classification from X-rays.  ...  Moran, “Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks.” 2017. [19] T. J. R. E. L. P. S. S.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.14292v1">arXiv:2106.14292v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d7p4zyzwtzd55josumla63bbiy">fatcat:d7p4zyzwtzd55josumla63bbiy</a> </span>
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Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks

Joseph Antony, Kevin McGuinness, Noel E O'Connor, Kieran Moran
<span title="">2016</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jsl2pgelqja2piczru3a6nqkg4" style="color: black;">2016 23rd International Conference on Pattern Recognition (ICPR)</a> </i> &nbsp;
This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN).  ...  We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images.  ...  the Department of Health and Human Services, and conducted by the OAI Study Investigators.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icpr.2016.7899799">doi:10.1109/icpr.2016.7899799</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icpr/AntonyMOM16.html">dblp:conf/icpr/AntonyMOM16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xhof7ht32ncmbnaitufklubp64">fatcat:xhof7ht32ncmbnaitufklubp64</a> </span>
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Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks [article]

Joseph Antony, Kevin McGuinness, Noel E O Connor, Kieran Moran
<span title="2016-09-08">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN).  ...  We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images.  ...  the Department of Health and Human Services, and conducted by the OAI Study Investigators.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1609.02469v1">arXiv:1609.02469v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/54umrxojhnhjpppeecc223vxqq">fatcat:54umrxojhnhjpppeecc223vxqq</a> </span>
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Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Pauline Shan Qing Yeoh, Khin Wee Lai, Siew Li Goh, Khairunnisa Hasikin, Yan Chai Hum, Yee Kai Tee, Samiappan Dhanalakshmi, Bai Yuan Ding
<span title="2021-11-10">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3wwzxqpotbc73bzpemzybzg7ee" style="color: black;">Computational Intelligence and Neuroscience</a> </i> &nbsp;
Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical  ...  We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field.  ...  Acknowledgments e authors would like to acknowledge the funding support of Ministry of Higher Education Malaysia and Universiti Malaya under FRGS project (FRGS/1/2018/TK04/UM/02/9) and UTAR Research Fund  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/4931437">doi:10.1155/2021/4931437</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34804143">pmid:34804143</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8598325/">pmcid:PMC8598325</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ctxoil3765cklnvwksyflrepe4">fatcat:ctxoil3765cklnvwksyflrepe4</a> </span>
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DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis from Radiographs and Magnetic Resonance Imaging

Md. Rezaul Karim, Jiao Jiao, Till Dohmen, Michael Cochez, Oya Beyan, Dietrich Rebholz-Schuhmann, Stefan Decker
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Knee osteoarthritis, biomedical imaging, deep neural networks, neural ensemble, explainability, Grad-CAM++, layer-wise relevance propagation.  ...  and efficient early quantification of OA.  ...  ACKNOWLEDGMENT The authors would like to thank the support of the Nvidia Corporation with the donation of the TITAN Xp GPU for this research.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3062493">doi:10.1109/access.2021.3062493</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/74npj3uinzhypoqcrn36dofare">fatcat:74npj3uinzhypoqcrn36dofare</a> </span>
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Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs Using Deep Convolutional Neural Networks

Aleksei Tiulpin, Simo Saarakkala
<span title="2020-11-10">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/abr6zdbfebblxlzh436eqtiagi" style="color: black;">Diagnostics</a> </i> &nbsp;
This provides a fine-grained OA severity assessment of the knee, compared to the goldstandard and most commonly used Kellgren–Lawrence (KL) composite score.  ...  independent assessment of knee osteophytes, joint space narrowing and other kneefeatures.  ...  Conflicts of Interest: The authors declare no competing interests Abbreviations The following abbreviations are used in this manuscript: OAI Osteoarthritis Initiative MOST Multicenter Osteoarthritis  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/diagnostics10110932">doi:10.3390/diagnostics10110932</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33182830">pmid:33182830</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7697270/">pmcid:PMC7697270</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uchm4f2szbbdhocs5ogfzgcela">fatcat:uchm4f2szbbdhocs5ogfzgcela</a> </span>
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Machine Learning in Knee Osteoarthritis: A Review

C. Kokkotis, S. Moustakidis, E. Papageorgiou, G. Giakas, D.E. Tsaopoulos
<span title="">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pwyxcghj2raa5iwyy575rfpwd4" style="color: black;">Osteoarthritis and Cartilage Open</a> </i> &nbsp;
machine learning, deep learning and knee osteoarthritis were used.  ...  DL models can be either supervised, partially supervised, or even unsupervised. 115 Convolutional neural networks (CNN) are among the most famous DL networks where 116 feature maps are extracted by performing  ...  ., Automatic detection of knee joints and quantification of knee 695 Computer Assisted Radiology and Surgery: p. 1-10. 703 120.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ocarto.2020.100069">doi:10.1016/j.ocarto.2020.100069</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gozdl73zu5hfhal37i3oi5wxri">fatcat:gozdl73zu5hfhal37i3oi5wxri</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200506072456/https://pdf.sciencedirectassets.com/321104/AIP/1-s2.0-S2665913120300583/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGcaCXVzLWVhc3QtMSJHMEUCIC3e6HjBwgVPTxL%2Bu5CuPOGvylWAxPfvbVBoZtMCyEUuAiEA9xF5uAJt9gXFJfn339OUr3KiLR%2FWtaOLZSky4z6hrhwqvQMIn%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARADGgwwNTkwMDM1NDY4NjUiDJ7xfd%2F3DdngBCYkjiqRA2xWDt9NajAbNOXYjaXMdhLZbYGVbjjN09bdrtVLrc6uDT5MPemaNnlCgGeGG4OEQbstrbupWlCNl9xn1zeKtn8UjC8v4Es4L2v8kWnGVevChhOJADicmsUaIZX7vPqueXS13PG0ROlEY5SOTTwAK4g0jlvXbnY2OAplZLdESGM7fPcW6dZKRL83afEqRy9PHuViBxVu9i%2FV5Js5xkP%2BX7fi9jr%2BNea%2FuwMAiHTQLIVRkiq0zbqKMhCNWZOhd6WkkU5VC%2Bxd7%2FS%2FZ%2Fqdy14qRhFWEph1PjrNFOIWNBfGcmH%2Fx7sx4sgmmqdaubs7n4XcJzqRzyDFQM4FaXr0Mw6By7%2F8D%2FVklXfioFxOOS3smydsAwwJbzipqroioPYerqFrMlEwwhDTmII7drwlUFDHtiJShFkj%2FDwnIRIs3ebq11GBq90W%2BR%2BDKt3oz8cnFpFjUigXznkvLHe1lIu1vnocvc9dLbP8ym5ZTG36vi5QcDHfePL0kRJJY%2BVfjg9PTampqMe8vkQ00jjy7W%2BAAb%2BmcUqwMImqyfUFOusBbpVyw2p45e6hRD%2BBZm3JDo9e%2FSAzYWX3Jpkh45hzDGxkRnmLeFj%2BIbgibYKaVlCUcDTOIEJsTsagBAr%2FbgJGL0v0%2FUk7XNpA8k8TqqYvL%2BmAO0kJCQJCr%2BoF1OUS9t2RyukiI6E2HVk%2B7zal3gXRBSOY9VUBx3xicVMeoNslHiECtd3FN7dWsFpmxjZnGWsHTUif1%2BU%2FGmN389UL5WEKOfKL20VKxy%2Bxxv5Ntb8u1diIYxGPXJcssenU6TbINDZP15oht6JeZn8EKHuFuWMjVNBcApGg1RbMkzI%2BkA6GS%2FOjI1x%2Fqd%2BgIYEb%2FA%3D%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20200506T072440Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTYX42RIPUX%2F20200506%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=1e80d56cfa31ff1e0312458fb772e436492c7ed6e0b592e85118091b537b5eac&amp;hash=1ccffbe4eceabd80a8a80873557f60c5fcaf133ea3830ebf8570897c9d15b742&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S2665913120300583&amp;tid=spdf-0adce795-4b91-4735-a4ce-202df4e7347d&amp;sid=5ea722358f86d5405279495-04f9f86fcc28gxrqa&amp;type=client" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ocarto.2020.100069"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

The Use of Artificial Intelligence in the Evaluation of Knee Pathology

Elisabeth R. Garwood, Ryan Tai, Ganesh Joshi, George J. Watts V
<span title="2020-01-28">2020</span> <i title="Georg Thieme Verlag KG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4bqtini2lveelm3df3c2j6a3cy" style="color: black;">Seminars in Musculoskeletal Radiology</a> </i> &nbsp;
Experimental algorithms have already been developed that can assess the severity of knee osteoarthritis from radiographs, detect and classify cartilage lesions, meniscal tears, and ligament tears on magnetic  ...  resonance imaging, provide automatic quantitative assessment of tendon healing, detect fractures on radiographs, and predict those at highest risk for recurrent bone tumors.  ...  Antony et al introduced the concept of an "end-to-end" AI model for the automatic radiographic grading of knee OA based on the KL scale. 13 By first using a convolutional neural network, the authors  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1055/s-0039-3400264">doi:10.1055/s-0039-3400264</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31991449">pmid:31991449</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e4jdhobrgvhfjj7nv5j7ssronu">fatcat:e4jdhobrgvhfjj7nv5j7ssronu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200710183146/https://www.thieme-connect.com/products/ejournals/pdf/10.1055/s-0039-3400264.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d2/6a/d26a3879e5f63b02b460efe5ea611fdb4e095376.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1055/s-0039-3400264"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Coherence Learning using Keypoint-based Pooling Network for Accurately Assessing Radiographic Knee Osteoarthritis [article]

Kang Zheng, Yirui Wang, Chen-I Hsieh, Le Lu, Jing Xiao, Chang-Fu Kuo, Shun Miao
<span title="2021-12-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Knee osteoarthritis (OA) is a common degenerate joint disorder that affects a large population of elderly people worldwide.  ...  Accurate radiographic assessment of knee OA severity plays a critical role in chronic patient management.  ...  .: Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.09177v1">arXiv:2112.09177v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o7bx5n7nkbhttju4aucqe3jbam">fatcat:o7bx5n7nkbhttju4aucqe3jbam</a> </span>
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A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to Deep Learning

Sozan Mohammed Ahmed, Ramadhan J. Mstafa
<span title="2022-03-01">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/abr6zdbfebblxlzh436eqtiagi" style="color: black;">Diagnostics</a> </i> &nbsp;
Knee osteoarthritis (KOA) is a degenerative joint disease, which significantly affects middle-aged and elderly people.  ...  In knee joint research and clinical practice there are segmentation approaches that play a significant role in KOA diagnosis and categorization.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/diagnostics12030611">doi:10.3390/diagnostics12030611</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35328164">pmid:35328164</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8946914/">pmcid:PMC8946914</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v42arjc75vh3lll42a5yz6ncnm">fatcat:v42arjc75vh3lll42a5yz6ncnm</a> </span>
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Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression

James Chung-Wai Cheung, Andy Yiu-Chau Tam, Lok-Chun Chan, Ping-Keung Chan, Chunyi Wen
<span title="2021-10-27">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/j5h4zct24vcofi5scx6ttoapnq" style="color: black;">Biology</a> </i> &nbsp;
We compared the prediction efficiency of the multiple-joint space width (JSW) and the minimum-JSW on knee osteoarthritis (KOA) severity and progression by using a deep learning approach.  ...  A convolutional neural network (CNN) with ResU-Net architecture was developed for knee X-ray imaging segmentation and has attained a segmentation efficiency of 98.9% intersection over union (IoU) on the  ...  Meanwhile, Lindner et al. and Thomson et al. employed convolutional neural networks (CNN) to create a bounding box to localize the joint space for subsequent detailed grading [15, 16] .  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211027060641/https://mdpi-res.com/d_attachment/biology/biology-10-01107/article_deploy/biology-10-01107.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/08/04/0804c3a21cf472fe3b0cb8ab95b28c8ac39aee8a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/biology10111107"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614846" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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