3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients

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.

Targeted exercises can improve biomechanical markers in individuals with knee osteoarthritis: A secondary analysis from a cluster randomized controlled trial

“…This is notable knowing that some of these patient characteristics have been previously reported to influence the efficacy of other exercise programs on clinical outcomes [48][49]. Knowing that biomechanical markers have been used to predict response to such exercises [50], results from this study reinforce the value of integrating them in knee OA patient management.…”

Evidence for key individual characteristics associated with outcomes following combined first-line interventions for knee osteoarthritis: A systematic review

“…Reasons for exclusion were the intervention was defined retrospectively from electronic health records [49]; or retrospectively by participant self-report [50,51], an exercise intervention only [52][53][54][55][56], including exercise and manual therapy [57] or the addition of joint injections with exercise and education component [58]. Other reasons included the study used analysis of variance and we were unable to extract meaningful estimates [59,60], did not report an association measure [61] or used individual participant data (IPD) from 7 RCTs that evaluated a range of interventions and different musculoskeletal conditions, including knee osteoarthritis [62].…”

Towards identifying key features in the classification of knee osteoarthritis – An enhanced feature fusion based deep network model

“…Various researchers have applied Machine Learning (ML) models for diagnosing KOA [14][15][16] . Several classification models have been proposed such as Artificial Neural Network (ANN) classifier 17 , logistic regression, Support Vector Machine (SVM) classifier 18 , decision trees 19 , random forest 20 , etc. However, it can be inferred from these studies that detecting symptoms and risks, causing OA is a complex task.…”