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Simple Summary  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.    Abstract  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.

biology

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