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Osteoarthritis is one of the leading causes of disability around the globe. Up to this
date there is no definite cure for cartilage lesions. Only fast and accurate diagnosis
enables prolonging joint survivor time. Available diagnostic methods have
disadvantages such as high price, radiation, need for experienced radiologists or low
availability in some regions. The present study evaluates the use of vibroarthorgraphy
as a method of cartilage lesion detection. 47 patients with diagnosed cartilage lesions,
and 51 healthy control group patients have been enrolled in this study. The cartilage
in the study group was evaluated intraoperatively by experienced orthopaedic surgeon.
Signal acquisition was performed in open and closed kinematic chain based on 10 knee
joint movements from 0-90 degrees. By using EEMD-DFA algorithms, reducing
classifier inputs using ANOVA and then classifying using artificial neural networks
(ANN), a classification accuracy of almost 93% was achieved. A sensitivity of 0.93 and
a specificity of 0.93 with an AUC of 0.942 were obtained for the multilayer perceptron
network. These results allow to apply this testing protocol in a clinical setting in the
future.
