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

Biology

卷:10

Andy Yiu-Chau Tam¹  James Chung-Wai Cheung¹  Chunyi Wen¹  Lok-Chun Chan¹  Ping-Keung Chan² 

[1] Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong, China;

[2] Department of Orthopaedics and Traumatology, Queen Mary Hospital, Hong Kong, China;

关键词: knee osteoarthritis;    deep learning;    automatic measurement;    joint space width;    musculoskeletal disorders;    Kellgren-Lawrence grade;   

DOI  :  10.3390/biology10111107

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown