MACHINE LEARNING-BASED AUTOMATIC CLASSIFICATION OF KNEE OSTEOARTHRITIS SEVERITY USING GAIT DATA AND RADIOGRAPHIC IMAGES
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.
? In existing techniques, single or separate feature descriptors have been used that somehow fail to classify all grades of KOA due to KL having more than 95% accuracy.
? To facilitate the development of treatments, there is a need to make disease staging more efficient. Several methods currently exist for OA staging.
? More accurate, consistent diagnosis of OA stage would ensure that investigative treatments are evaluated in patients in the severity range intended by the investigators.
? These, bone diseases, including Knee Osteoarthritis (KOA), have a great impact on quality of life.
? KOA is a knee joint problem mainly produced due to decreased Articular Cartilage between femur and tibia bones, producing severe joint pain, effusion, joint movement constraints and gait anomalies.
? To address these issues, this study presents a novel KOA detection at early stages using deep learning-based feature extraction and classification.
• In our proposed technique, both low and high-level features are used for the resultant image that outperforms the state-of-the-art by complete matching with the trained knee features.
• The proposed model feature is extracted from the region of interest using joint space width by CNN with LBP and CNN with HOG. The multi-class classifiers, that is, SVM, RF, and KNN, are used to classify KOA according to the KL system.
• The proposed model can also be merged with models for the hybrid and diverse detection of different diseases other than of the knee.
? Deep learning is an effective technique for the analysis and classification of images, which is widely applied in various fields such as the medical field, and demonstrates excellent performances.
? The deep learning approach did not demonstrate a satisfactory performance when it was applied to the classification of KOA based on radiographic images.
? However, the classification performance of the deep learning network based on radiographic images was not sufficient for the accurate diagnosis of KOA using the KL grading system.
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