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The research study was financed in the framework of the project Lublin University of Technology-Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education (contract no. 030/RID/2018/19)
Osteoarthritis (OA) is a chronic, progressive disease which has over 300 million cases each
year. Some of the main symptoms of OA are pain, restriction of joint motion and stiffness of the joint.
Early diagnosis and treatment can prolong painless joint function. Vibroarthrography (VAG) is a
cheap, reproducible, non-invasive and easy-to-use tool which can be implemented in the diagnostic
route. The aim of this study was to establish diagnostic accuracy and to identify the most accurate
signal processing method for the detection of OA in knee joints. In this study, we have enrolled a total
of 67 patients, 34 in a study group and 33 in a control group. All patients in the study group were
referred for surgical treatment due to intraarticular lesions, and the control group consisted of healthy
individuals without knee symptoms. Cartilage status was assessed during surgery according to the
International Cartilage Repair Society (ICRS) and vibroarthrography was performed one day prior to
surgery in the study group. Vibroarthrography was performed in an open and closed kinematic chain
for the involved knees in the study and control group. Signals were acquired by two sensors placed on
the medial and lateral joint line. Using the neighbourhood component analysis (NCA) algorithm, the
selection of optimal signal measures was performed. Classification using artificial neural networks
was performed for three variants: I—open kinetic chain, II—closed kinetic chain, and III—open and
closed kinetic chain. Vibroarthrography showed high diagnostic accuracy in determining healthy
cartilage from cartilage lesions, and the number of repetitions during examination can be reduced
only to closed kinematic chain.
