Next Article in Journal
Magnetic Resonance Assessment of Ejection Fraction Versus Echocardiography for Cardioverter-Defibrillator Implantation Eligibility
Next Article in Special Issue
Falls in Post-Polio Patients: Prevalence and Risk Factors
Previous Article in Journal
Zinc-Dependent Oligomerization of Thermus thermophilus Trigger Factor Chaperone
Previous Article in Special Issue
Gait Characteristics and Fatigue Profiles When Standing on Surfaces with Different Hardness: Gait Analysis and Machine Learning Algorithms
Article

Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression

1
Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong, China
2
Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China
3
Department of Orthopaedics and Traumatology, Queen Mary Hospital, Hong Kong, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Redha Taiar
Biology 2021, 10(11), 1107; https://doi.org/10.3390/biology10111107
Received: 23 September 2021 / Revised: 20 October 2021 / Accepted: 21 October 2021 / Published: 27 October 2021
(This article belongs to the Special Issue Human Bodywork: Applications in Health, Disease, and Rehabilitation)
Minimum-joint space width (JSW) is a prevalent clinical parameter in quantifying the joint space narrowing condition in knee osteoarthritis (KOA). In this study, we propose a novel multiple-JSW measurement, which is estimated by a deep learning-based model in an automated manner. The performance of the proposed automated measurement is found to be superior to the conventionally used minimum-JSW in the severity classification and progression prediction of KOA owing to the additional information of the joint space morphology encoded in the new approach. It is further demonstrated that the deep learning-based approach yields comparable performance as the measurement by radiologists. The approach presented in this work may lead to the development of a computer-aided tool for clinical practitioners that could facilitate the KOA diagnosis and prognosis with the fully automated, accurate, and efficient computation of the joint-space parameters.
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 distal femur and proximal tibia. Later, by leveraging the image segmentation, the minimum and multiple-JSWs in the tibiofemoral joint were estimated and then validated by radiologist measurements in the Osteoarthritis Initiative (OAI) dataset using Pearson correlation and Bland–Altman plots. The agreement between the CNN-based estimation and radiologist’s measurement of minimum-JSWs reached 0.7801 (p < 0.0001). The estimated JSWs were deployed to predict the radiographic severity and progression of KOA defined by Kellgren-Lawrence (KL) grades using the XGBoost model. The 64-point multiple-JSWs achieved the best performance in predicting KOA progression within 48 months, with the area-under-receiver operating characteristic curve (AUC) of 0.621, outperforming the commonly used minimum-JSW with 0.554 AUC. We provided a fully automated radiographic assessment tool for KOA with comparable performance to the radiologists and showed that the fine-grained measurement of multiple-JSWs yields superior prediction performance for KOA over the minimum-JSW. View Full-Text
Keywords: knee osteoarthritis; deep learning; automatic measurement; joint space width; musculoskeletal disorders; Kellgren-Lawrence grade knee osteoarthritis; deep learning; automatic measurement; joint space width; musculoskeletal disorders; Kellgren-Lawrence grade
Show Figures

Figure 1

MDPI and ACS Style

Cheung, J.C.-W.; Tam, A.Y.-C.; Chan, L.-C.; Chan, P.-K.; Wen, C. Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression. Biology 2021, 10, 1107. https://doi.org/10.3390/biology10111107

AMA Style

Cheung JC-W, Tam AY-C, Chan L-C, Chan P-K, Wen C. Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression. Biology. 2021; 10(11):1107. https://doi.org/10.3390/biology10111107

Chicago/Turabian Style

Cheung, James C.-W., Andy Y.-C. Tam, Lok-Chun Chan, Ping-Keung Chan, and Chunyi Wen. 2021. "Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression" Biology 10, no. 11: 1107. https://doi.org/10.3390/biology10111107

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop