Machine Learning-Based Automatic Classification of Knee Osteoarthritis Severity Using Gait Data and Radiographic Images
Soon Bin Kwon, Hyuk‐Soo Han, Myung Chul Lee, Hee Chan Kim, Yunseo Ku, Du Hyun Ro
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.