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

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...

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Autores principales:	James Chung-Wai Cheung, Andy Yiu-Chau Tam, Lok-Chun Chan, Ping-Keung Chan, Chunyi Wen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	knee osteoarthritis deep learning automatic measurement joint space width musculoskeletal disorders Kellgren-Lawrence grade Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4259a6d5900c453699c35cd04d8d38be
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spelling	oai:doaj.org-article:4259a6d5900c453699c35cd04d8d38be2021-11-25T16:47:04ZSuperiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression10.3390/biology101111072079-7737https://doaj.org/article/4259a6d5900c453699c35cd04d8d38be2021-10-01T00:00:00Zhttps://www.mdpi.com/2079-7737/10/11/1107https://doaj.org/toc/2079-7737We 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 (<i>p</i> < 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.James Chung-Wai CheungAndy Yiu-Chau TamLok-Chun ChanPing-Keung ChanChunyi WenMDPI AGarticleknee osteoarthritisdeep learningautomatic measurementjoint space widthmusculoskeletal disordersKellgren-Lawrence gradeBiology (General)QH301-705.5ENBiology, Vol 10, Iss 1107, p 1107 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	knee osteoarthritis deep learning automatic measurement joint space width musculoskeletal disorders Kellgren-Lawrence grade Biology (General) QH301-705.5
spellingShingle	knee osteoarthritis deep learning automatic measurement joint space width musculoskeletal disorders Kellgren-Lawrence grade Biology (General) QH301-705.5 James Chung-Wai Cheung Andy Yiu-Chau Tam Lok-Chun Chan Ping-Keung Chan Chunyi Wen Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression
description	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 (<i>p</i> < 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.
format	article
author	James Chung-Wai Cheung Andy Yiu-Chau Tam Lok-Chun Chan Ping-Keung Chan Chunyi Wen
author_facet	James Chung-Wai Cheung Andy Yiu-Chau Tam Lok-Chun Chan Ping-Keung Chan Chunyi Wen
author_sort	James Chung-Wai Cheung
title	Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression
title_short	Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression
title_full	Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression
title_fullStr	Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression
title_full_unstemmed	Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression
title_sort	superiority of multiple-joint space width over minimum-joint space width approach in the machine learning for radiographic severity and knee osteoarthritis progression
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4259a6d5900c453699c35cd04d8d38be
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_version_	1718412950086090752

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

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