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Emergence of deep learning in knee osteoarthritis diagnosis

Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly...

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Main Authors:	Yeoh, Pauline Shan Qing, Lai, Khin Wee, Goh, Siew Li, Hasikin, Khairunnisa, Hum, Yan Chai, Tee, Yee Kai, Dhanalakshmi, Samiappan
Format:	Article
Published:	Hindawi 2021
Subjects:	QH301 Biology RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Online Access:	http://eprints.um.edu.my/28673/
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id	my.um.eprints.28673
record_format	eprints
spelling	my.um.eprints.286732022-04-06T04:36:00Z http://eprints.um.edu.my/28673/ Emergence of deep learning in knee osteoarthritis diagnosis Yeoh, Pauline Shan Qing Lai, Khin Wee Goh, Siew Li Hasikin, Khairunnisa Hum, Yan Chai Tee, Yee Kai Dhanalakshmi, Samiappan QH301 Biology RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. 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 workflow efficiency. Medical imaging processes, especially those that produce 3-dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state-of-the-art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field. Hindawi 2021-11-10 Article PeerReviewed Yeoh, Pauline Shan Qing and Lai, Khin Wee and Goh, Siew Li and Hasikin, Khairunnisa and Hum, Yan Chai and Tee, Yee Kai and Dhanalakshmi, Samiappan (2021) Emergence of deep learning in knee osteoarthritis diagnosis. Computational Intelligence and Neuroscience, 2021. ISSN 1687-5265, DOI https://doi.org/10.1155/2021/4931437 <https://doi.org/10.1155/2021/4931437>. 10.1155/2021/4931437
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QH301 Biology RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
spellingShingle	QH301 Biology RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry Yeoh, Pauline Shan Qing Lai, Khin Wee Goh, Siew Li Hasikin, Khairunnisa Hum, Yan Chai Tee, Yee Kai Dhanalakshmi, Samiappan Emergence of deep learning in knee osteoarthritis diagnosis
description	Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. 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 workflow efficiency. Medical imaging processes, especially those that produce 3-dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state-of-the-art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.
format	Article
author	Yeoh, Pauline Shan Qing Lai, Khin Wee Goh, Siew Li Hasikin, Khairunnisa Hum, Yan Chai Tee, Yee Kai Dhanalakshmi, Samiappan
author_facet	Yeoh, Pauline Shan Qing Lai, Khin Wee Goh, Siew Li Hasikin, Khairunnisa Hum, Yan Chai Tee, Yee Kai Dhanalakshmi, Samiappan
author_sort	Yeoh, Pauline Shan Qing
title	Emergence of deep learning in knee osteoarthritis diagnosis
title_short	Emergence of deep learning in knee osteoarthritis diagnosis
title_full	Emergence of deep learning in knee osteoarthritis diagnosis
title_fullStr	Emergence of deep learning in knee osteoarthritis diagnosis
title_full_unstemmed	Emergence of deep learning in knee osteoarthritis diagnosis
title_sort	emergence of deep learning in knee osteoarthritis diagnosis
publisher	Hindawi
publishDate	2021
url	http://eprints.um.edu.my/28673/
_version_	1735409569097580544
score	13.211869

Emergence of deep learning in knee osteoarthritis diagnosis

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