Abstract
Measuring knee biomechanics provides valuable clinical information for defining patient-specific treatment options, including patient-oriented physical exercise programs. It can be done by a knee kinesiography test measuring the three-dimensional rotation angles (3D kinematics) during walking, thus providing objective knowledge about knee function in dynamic and weight-bearing conditions. The purpose of this study was to assess whether 3D kinematics can be efficiently used to predict the impact of a physical exercise program on the condition of knee osteoarthritis (OA) patients. The prediction was based on 3D knee kinematic data, namely flexion/extension, adduction/abduction and external/internal rotation angles collected during a treadmill walking session at baseline. These measurements are quantifiable information suitable to develop automatic and objective methods for personalized computer-aided treatment systems. The dataset included 221 patients who followed a personalized therapeutic physical exercise program for 6 months and were then assigned to one of two classes, Improved condition (I) and not-Improved condition (nI). A 10% improvement in pain was needed at the 6-month follow-up compared to baseline to be in the improved group. The developed model was able to predict I and nI with 84.4% accuracy for men and 75.5% for women using a decision tree classifier trained with 3D knee kinematic data taken at baseline and a 10-fold validation procedure. The models showed that men with an impaired control of their varus thrust and a higher pain level at baseline, and women with a greater amplitude of internal tibia rotation were more likely to report improvements in their pain level after 6 months of exercises. Results support the effectiveness of decision trees and the relevance of 3D kinematic data to objectively predict knee OA patients’ response to a treatment consisting of a physical exercise program.
Highlights
Introduction nal affiliationsThe knee is an anatomically and biomechanically complex joint that serves as the basis for the mobility and stability of the human body
The methodology used is described in the block diagram presented in Figure 2 and involves the following steps: (1) establishing a database of knee OA patients with 3D knee kinematics measurements at baseline and at the completion of a 6-month exercise program, (2) identifying patients according to the improvement in their condition, (3) extracting biomechanical factors from their kinematic data, and (4) building a prediction model based on decision trees
The demographic characteristics and one clinical feature, the radiographic OA severity grade measured by the Kellgren-Lawrence scale (KL; grade 2: mild; grade 3: moderate; grade 4: severe) [19] were collected for all participants
Summary
The knee is an anatomically and biomechanically complex joint that serves as the basis for the mobility and stability of the human body. This joint undergoes various static and dynamic stresses that make it subject to several degenerative diseases, including knee osteoarthritis (OA). The World Health Organization (WHO) estimates that 10% of the adult population in developed countries suffers from OA, 6.1% of which affects the knee [1]. In Canada, hundreds of thousands of people suffer from knee OA, which affects their functional abilities and undermines their quality of life [2]. Osteoarthritis can be diagnosed by a physician
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