Abstract
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.
Highlights
Knee joint is the biggest joint in the human body; it is one of the most important joints in daily living
For variant I, the following measures of acoustic signals were selected based on the analyses performed: peak value, peak-to-peak value, impact factor, shape factor, variance, M6A, and M8A
These measures were used as input data for variant I classification
Summary
Knee joint is the biggest joint in the human body; it is one of the most important joints in daily living. Detection of OA can be achieved by measuring vibrations and sounds generated by the joint during motion, under the premise that smooth, optimally lubricated cartilage surfaces move quietly relative to each other, while rough, sub-optimally lubricated surfaces move unevenly, generating more acoustic signals and vibrations, commonly referred to as crepitus [17,26,27]. These signals are generated by transient elastic waves resulting from sudden stress redistribution in the material and can be recorded from the surface of the knee [17]. The aim of this study was to compare selected indices of acoustic signals recorded in a group of patients qualified for surgery with intraoperative confirmation of the extent of cartilage damage
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