3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients
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...
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| Format: | Article |
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MDPI AG
2021-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/2/834 |
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| author | Marwa Mezghani Nicola Hagemeister Youssef Ouakrim Alix Cagnin Alexandre Fuentes Neila Mezghani |
| author_facet | Marwa Mezghani Nicola Hagemeister Youssef Ouakrim Alix Cagnin Alexandre Fuentes Neila Mezghani |
| author_sort | Marwa Mezghani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:32:30Z |
| format | Article |
| id | doaj.art-f7d056d17ea04ad5a90c2f02c9fd350f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:32:30Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f7d056d17ea04ad5a90c2f02c9fd350f2023-12-03T13:34:44ZengMDPI AGApplied Sciences2076-34172021-01-0111283410.3390/app110208343D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis PatientsMarwa Mezghani0Nicola Hagemeister1Youssef Ouakrim2Alix Cagnin3Alexandre Fuentes4Neila Mezghani5École Supérieure de la Statistique et de L’analyse de L’information de Tunis, Université de Carthage, 1080 Tunis, TunisieLaboratoire de Recherche en Imagerie et Orthopédie (LIO), École de Technologie Supérieure (ETS), CRCHUM, Montréal, QC H2X 0A9, CanadaLICEF Institute, TELUQ University, Montréal, QC H2S 3L5, CanadaLaboratoire de Recherche en Imagerie et Orthopédie (LIO), École de Technologie Supérieure (ETS), CRCHUM, Montréal, QC H2X 0A9, CanadaEMOVI Inc., Montreal, QC H2S 1B1, CanadaLICEF Institute, TELUQ University, Montréal, QC H2S 3L5, CanadaMeasuring 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.https://www.mdpi.com/2076-3417/11/2/8343D kinematicsdecision treesknee osteoarthritisphysical exercises |
| spellingShingle | Marwa Mezghani Nicola Hagemeister Youssef Ouakrim Alix Cagnin Alexandre Fuentes Neila Mezghani 3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients Applied Sciences 3D kinematics decision trees knee osteoarthritis physical exercises |
| title | 3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients |
| title_full | 3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients |
| title_fullStr | 3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients |
| title_full_unstemmed | 3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients |
| title_short | 3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients |
| title_sort | 3d kinematics and decision trees to predict the impact of a physical exercise program on knee osteoarthritis patients |
| topic | 3D kinematics decision trees knee osteoarthritis physical exercises |
| url | https://www.mdpi.com/2076-3417/11/2/834 |
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