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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Autores principales:	Pauline Shan Qing Yeoh, Khin Wee Lai, Siew Li Goh, Khairunnisa Hasikin, Yan Chai Hum, Yee Kai Tee, Samiappan Dhanalakshmi
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/abded4b17e56437ea8099beb21786a7a
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spelling	oai:doaj.org-article:abded4b17e56437ea8099beb21786a7a2021-11-22T01:09:35ZEmergence of Deep Learning in Knee Osteoarthritis Diagnosis1687-527310.1155/2021/4931437https://doaj.org/article/abded4b17e56437ea8099beb21786a7a2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4931437https://doaj.org/toc/1687-5273Osteoarthritis (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.Pauline Shan Qing YeohKhin Wee LaiSiew Li GohKhairunnisa HasikinYan Chai HumYee Kai TeeSamiappan DhanalakshmiHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Pauline Shan Qing Yeoh Khin Wee Lai Siew Li Goh Khairunnisa Hasikin Yan Chai Hum Yee Kai Tee Samiappan Dhanalakshmi 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	Pauline Shan Qing Yeoh Khin Wee Lai Siew Li Goh Khairunnisa Hasikin Yan Chai Hum Yee Kai Tee Samiappan Dhanalakshmi
author_facet	Pauline Shan Qing Yeoh Khin Wee Lai Siew Li Goh Khairunnisa Hasikin Yan Chai Hum Yee Kai Tee Samiappan Dhanalakshmi
author_sort	Pauline Shan Qing Yeoh
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 Limited
publishDate	2021
url	https://doaj.org/article/abded4b17e56437ea8099beb21786a7a
work_keys_str_mv	AT paulineshanqingyeoh emergenceofdeeplearninginkneeosteoarthritisdiagnosis AT khinweelai emergenceofdeeplearninginkneeosteoarthritisdiagnosis AT siewligoh emergenceofdeeplearninginkneeosteoarthritisdiagnosis AT khairunnisahasikin emergenceofdeeplearninginkneeosteoarthritisdiagnosis AT yanchaihum emergenceofdeeplearninginkneeosteoarthritisdiagnosis AT yeekaitee emergenceofdeeplearninginkneeosteoarthritisdiagnosis AT samiappandhanalakshmi emergenceofdeeplearninginkneeosteoarthritisdiagnosis
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Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

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