Emergence of deep learning in knee osteoarthritis diagnosis

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.

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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.

Item Type: Article
Funders: Ministry of Education, Malaysia, Universiti Malaya under FRGS project (FRGS/1/2018/TK04/UM/02/9), UTAR Research Fund (IPSR/RMC/UTARRF/2020-C1/H02)
Uncontrolled Keywords: Cartilage; Progression; Networks
Subjects: Q Science > QH Natural history > QH301 Biology
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 06 Apr 2022 04:36
Last Modified: 06 Apr 2022 04:36
URI: http://eprints.um.edu.my/id/eprint/28673

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