Abstract
Knee osteoarthritis (KOA) is a leading cause of disability among elderly adults, and it causes pain and discomfort and limits the functional independence of such adults. The aim of this study was the development of an automated classification model for KOA, based on the Kellgren-Lawrence (KL) grading system, using radiographic imaging and gait analysis data. Gait features highly associated with the radiological severity of KOA identified from our previous study, in addition to radiographic image features extracted from a deep learning network, namely, Inception-ResNet-v2, were exploited using a support vector machine for KOA multi-classification. The area under the curve (AUC) of the receiver operating characteristic curve from KL Grades 0-4 were 0.93, 0.82, 0.83, 0.88, and 0.97, respectively. The sensitivity, precision, and F1-score of the model were 0.70, 0.76, and 0.71, respectively. The proposed model outperformed a common deep learning approach that is based on using only radiographic images as the input data. This result indicates that gait data and radiographic images are complementary with respect to KOA classification, and the use of both data can improve the accuracy of the automated diagnosis of multiclass KOA.
Highlights
Osteoarthritis (OA) is a leading cause of disability among elderly adults, which affects 30% of the global population over the age of 60 years old [1]
DEEP LEARNING APPROACH BASED ON RADIOGRAPHICAL IMAGES Fig. 2a presents an example cropped radiographic image of the knee used as an input for both radiographic image feature extraction and deep learning model
This study demonstrated that the machine learning approach based on gait and radiographic image features can improve the classification performance of knee osteoarthritis (KOA) at the KL grading scale when compared with the use of only radiographic images
Summary
Osteoarthritis (OA) is a leading cause of disability among elderly adults, which affects 30% of the global population over the age of 60 years old [1]. With the aging of the global population, the number of patients who suffer from knee osteoarthritis (KOA) is expected to increase [4]. The typical symptoms of KOA include pain, stiffness, decreased joint range of motion, and gait dysfunctions, which worsen in accordance with an increase in the disease progression [5], [6]. The current gold standard for the radiographic assessment of KOA is the Kellgren–Lawrence (KL) grading system [7]. The KL grading system is widely implemented in clinical applications for the diagnosis of KOA, it is time consuming and requires highly trained experts, generally with fellowship training experiences in arthroplasty or radiography [8].
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