A novel computer-assisted diagnosis method of knee osteoarthritis based on multivariate information and deep learning model

Knee Osteoarthritis (KOA) is a chronic joint disease characterized by degeneration of knee's articular cartilage. Comparing with the imaging diagnostic method, the vibroarthrographic (VAG) may be emerged as a new candidate for diagnosis of KOA in clinics. However, it is challenging for doctors to evaluate patients' condition by visually detecting VAGs due to the limited understanding about contained information. Besides, the basic physiological signals are also closely correlated with a greater risk of KOA. Based on this, we focus on studying computer-assisted diagnosis method of KOA (KOA-CAD) using multivariate information (i.e. VAGs and basic physiological signals) based on an improved deep learning model (DLM). Firstly, a new Laplace distribution-based strategy (LD-S) for classification in DLM is designed. Secondly, an aggregated multiscale dilated convolution network (AMD-CNN) is constructed to learn features from multivariate information of KOA patients. Then, a new KOA-CAD method is proposed by integrating the AMD-CNN with the LD-S to realize three CAD objectives, including the automatic KOA detection, the KOA early detection, and the KOA grading detection. Finally, the multivariate information collected clinically is applied to verify the proposed method, where the automatic detection accuracy, early detection accuracy and grading detection accuracy are 93.6%, 92.1%, and 84.2% respectively.(c) 2022 Elsevier Inc. All rights reserved.