A novel automatic Knee Osteoarthritis detection method based on vibroarthrographic signals

Knee Osteoarthritis (KOA) is a common and chronic degenerative joint disease. Comparing with the traditional examinations (e.g., X-ray, MRI), the vibroarthrographic (VAG) examination, which is a low-costly, atraumatic, and at-home way, may open up new alternatives to KOA detection in clinic. However, it is a hard problem for doctors to evaluate the condition of KOA patients by visually detecting VAG signal due to the very limited understanding of pathological information included in VAG signals. Originated from this, we focus on exploring a reliable KOA auxiliary diagnostic method using VAG signals. In this paper, a new feature extraction method is first proposed, where the kernel-radius-based feature (KR-F) and statistic-based feature (S-F) are extracted respectively in the transient phase space of VAG signal. Furthermore, two features are integrated in the featurefusion level (KR-S-FF), and then fed into the back propagation neural network (BPNN) to complete the KOA detection automatically. Finally, the proposed automatic KOA detection method is verified in a clinical VAG dataset, which is collected from one hospital in Xi'an, China. Simulation results convey that the proposed method gives the high detection accuracy of 98.2 % with sensitivity of 97.9 % and specificity of 98.5 %, showing that it may provide an effective non-invasive tool for KOA disorders.

Automated summary

This paper proposes a new auxiliary diagnosis method for KOA based on VAG signals, which achieves high accuracy, sensitivity, and specificity. The method extracts features, integrates them, and applies neural networks to achieve automatic KOA detection, providing an effective non-invasive tool for KOA disorders.