Emergence of deep learning in knee osteoarthritis diagnosis
Document Type
Article
Publication Date
11-10-2021
Abstract
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.
Keywords
Cartilage, Progression, Networks
Divisions
fac_eng
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)
Publication Title
Computational Intelligence and Neuroscience
Volume
2021
Publisher
Hindawi
Publisher Location
ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND