An analysis of 3D knee kinematic data complexity in knee osteoarthritis and asymptomatic controls

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.

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Knee osteoarthritis severity assessment using knee kinematic data classification

Osteoarthritis and Cartilage ◽

10.1016/j.joca.2012.02.102 ◽

2012 ◽

Vol 20 ◽

pp. S97 ◽

Cited By ~ 6

Author(s):

N. Mezghani ◽

Y. Ouakrim ◽

A. Fuentes ◽

N. Hagemeister ◽

R. Aissaoui ◽

...

Keyword(s):

Knee Osteoarthritis ◽

Data Classification ◽

Severity Assessment ◽

Knee Kinematic ◽

Kinematic Data

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Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals

Machine Learning and Knowledge Extraction ◽

10.3390/make1030045 ◽

2019 ◽

Vol 1 (3) ◽

pp. 768-784

Author(s):

Badreddine Ben Nouma ◽

Amar Mitiche ◽

Youssef Ouakrim ◽

Neila Mezghani

Keyword(s):

Knee Osteoarthritis ◽

Reference Data ◽

Small Sample ◽

Repeated Measurements ◽

Kinematic Data ◽

High Data ◽

Current State ◽

Pathology Classification ◽

Discrete Curves ◽

Knee Pathology

The analysis of knee kinematic data, which come in the form of a small sample of discrete curves that describe repeated measurements of the temporal variation of each of the knee three fundamental angles of rotation during a subject walking cycle, can inform knee pathology classification because, in general, different pathologies have different kinematic data patterns. However, high data dimensionality and the scarcity of reference data, which characterize this type of application, challenge classification and make it prone to error, a problem Duda and Hart refer to as the curse of dimensionality. The purpose of this study is to investigate a sample-based classifier which evaluates data proximity by the two-sample Hotelling T 2 statistic. This classifier uses the whole sample of an individual’s measurements for a better support to classification, and the Hotelling T 2 hypothesis testing made applicable by dimensionality reduction. This method was able to discriminate between femero-rotulian (FR) and femero-tibial (FT) knee osteoarthritis kinematic data with an accuracy of 88.1 % , outperforming significantly current state-of-the-art methods which addressed similar problems. Extended to the much harder three-class problem involving pathology categories FR and FT, as well as category FR-FT which represents the incidence of both diseases FR and FT in a same individual, the scheme was able to reach a performance that justifies its further use and investigation in this and other similar applications.

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Supervised Machine Learning for Aiding Diagnosis of Knee Osteoarthritis: A Systematic Review and Meta-Analysis

10.36227/techrxiv.13132931.v1 ◽

2020 ◽

Author(s):

Luca Parisi ◽

Narrendar RaviChandran ◽

Matteo Lanzillotta

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Knee Osteoarthritis ◽

Gait Analysis ◽

Meta Analysis ◽

Supervised Machine Learning ◽

Gait Patterns ◽

Kinematic Data ◽

Knee Oa ◽

Symptomatic Knee

Background Knee osteoarthritis (OA) remains a leading aetiology of disability worldwide. Clinical assessment of such knee-related conditions has improved with recent advances in gait analysis. Despite being a gold standard method, gait data acquired by motion capture (mocap) technology are highly non-linear and dimensional, which make traditional gait analysis challenging. Thus, extrinsic algorithms need to be used to make sense of gait data. Supervised Machine Learning (ML)-based classifiers outperform conventional statistical methods in revealing intrinsic patterns that can discern gait abnormalities when using mocap data, making them a suitable tool for aiding diagnosis of knee OA. Research question Studies have demonstrated the accuracy of supervised ML-based classifiers in gait analysis. However, these techniques have not gained wide acceptance amongst biomechanists for two reasons: the reliability of such methods has not been assessed and there is no consensus on which classifier or group of classifiers to select. Specifically, it is not clear whether classifiers that leverage optimal separating hyperplanes (OSH) or artificial neural networks (ANN) are more accurate and reliable. Methods A systematic review and meta-analysis were conducted to assess the capability of such algorithms to predict pathological kinematic and kinetic gait patterns as indicators of knee OA. With 153 eligible studies, 6 studies met the inclusion criteria for a subsequent meta-analysis, accounting for <a>273 healthy subjects and 313 patients </a>with symptomatic knee OA. The classification performance of supervised ML classifiers (OSH- or ANN-based) used in these studies was quantitatively assessed and compared across four following performance metrics: classification accuracy on the test set (ACC), sensitivity (SN), specificity (SP), and area under the receiver operating characteristic curve (AUC). Results There was no statistically significant discrepancy in the ACC between OSH- and ANN-based classifiers when dealing with kinetic and kinematic data concurrently, as well as when considering only kinematic data. However, there was a statistically significant difference in their SN and SP, with the ANN-based classifiers having higher SN and SP than OSH-based algorithms. As only one of the eligible studies reported AUC, this metric could not be assessed statistically across studies. Significance This study supports the use of ANN-based algorithms for classifying knee OA-related gait patterns as having a higher sensitivity and specificity than OSH-based classifiers. Considering their higher reliability, leveraging supervised ANN-based methods can aid biomechanists to diagnose knee OA objectively.

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A Combined Visualization Method for Multivariate Data Analysis. Application to Knee Kinematic and Clinical Parameters Relationships

Applied Sciences ◽

10.3390/app10051762 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1762

Author(s):

Fatima Bensalma ◽

Glen Richardson ◽

Youssef Ouakrim ◽

Alexandre Fuentes ◽

Michael Dunbar ◽

...

Keyword(s):

Canonical Correlation ◽

Multivariate Data Analysis ◽

Multivariate Data ◽

Demographic Characteristics ◽

Clinical Parameters ◽

Visualization Method ◽

Knee Kinematic ◽

Kinematic Data ◽

Analysis Application ◽

Multivariate Relationship

This paper aims to analyze the correlation structure between the kinematic and clinical parameters of an end-staged knee osteoarthritis population. The kinematic data are a set of characteristics derived from 3D knee kinematic patterns. The clinical parameters include the answers of a clinical questionnaire and the patient’s demographic characteristics. The proposed method performs, first, a regularized canonical correlation analysis (RCCA) to evaluate the multivariate relationship between the clinical and kinematic datasets, and second, a combined visualization method to better understand the relationships between these multivariate data. Results show the efficiency of using different and complementary visual representation tools to highlight hidden relationships and find insights in data.

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A variational method to determine the most representative shape of a set of curves and its application to knee kinematic data for pathology classification

Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence - MedPRAI '18 ◽

10.1145/3177148.3180095 ◽

2018 ◽

Cited By ~ 3

Author(s):

Badreddine Ben Nouma ◽

Neila Mezghani ◽

Amar Mitiche ◽

Youssef Ouakrim

Keyword(s):

Variational Method ◽

Knee Kinematic ◽

Kinematic Data ◽

Pathology Classification

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A Sample-Encoding Generalization of the Kohonen Associative Memory and Application to Knee Kinematic Data Representation and Pathology Classification

Applied Sciences ◽

10.3390/app9091741 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1741 ◽

Cited By ~ 3

Author(s):

Badreddine Ben Nouma ◽

Amar Mitiche ◽

Neila Mezghani

Keyword(s):

Three Dimensional ◽

Data Representation ◽

Small Sample ◽

Repeated Measurements ◽

Training Data ◽

Multidimensional Data ◽

Knee Kinematic ◽

Kinematic Data ◽

Pathology Classification ◽

Knee Pathology

Knee kinematic data consist of a small sample of high-dimensional vectors recording repeated measurements of the temporal variation of each of the three fundamental angles of knee three-dimensional rotation during a walking cycle. In applications such as knee pathology classification, the notorious problems of high-dimensionality (the curse of dimensionality), high intra-class variability, and inter-class similarity make this data generally difficult to interpret. In the face of these difficulties, the purpose of this study is to investigate knee kinematic data classification by a Kohonen neural network generalized to encode samples of multidimensional data vectors rather than single such vectors as in the standard network. The network training algorithm and its ensuing classification function both use the Hotelling T 2 statistic to evaluate the underlying sample similarity, thus affording efficient use of training data for network development and robust classification of observed data. Applied to knee osteoarthritis pathology discrimination, namely the femoro-rotulian (FR) and femoro-tibial (FT) categories, the scheme improves on the state-of-the-art methods.

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Supervised Machine Learning for Aiding Diagnosis of Knee Osteoarthritis: A Systematic Review and Meta-Analysis

10.36227/techrxiv.13132931 ◽

2020 ◽

Author(s):

Luca Parisi ◽

Narrendar RaviChandran ◽

Matteo Lanzillotta

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Knee Osteoarthritis ◽

Gait Analysis ◽

Meta Analysis ◽

Supervised Machine Learning ◽

Gait Patterns ◽

Kinematic Data ◽

Knee Oa ◽

Symptomatic Knee

Background Knee osteoarthritis (OA) remains a leading aetiology of disability worldwide. Clinical assessment of such knee-related conditions has improved with recent advances in gait analysis. Despite being a gold standard method, gait data acquired by motion capture (mocap) technology are highly non-linear and dimensional, which make traditional gait analysis challenging. Thus, extrinsic algorithms need to be used to make sense of gait data. Supervised Machine Learning (ML)-based classifiers outperform conventional statistical methods in revealing intrinsic patterns that can discern gait abnormalities when using mocap data, making them a suitable tool for aiding diagnosis of knee OA. Research question Studies have demonstrated the accuracy of supervised ML-based classifiers in gait analysis. However, these techniques have not gained wide acceptance amongst biomechanists for two reasons: the reliability of such methods has not been assessed and there is no consensus on which classifier or group of classifiers to select. Specifically, it is not clear whether classifiers that leverage optimal separating hyperplanes (OSH) or artificial neural networks (ANN) are more accurate and reliable. Methods A systematic review and meta-analysis were conducted to assess the capability of such algorithms to predict pathological kinematic and kinetic gait patterns as indicators of knee OA. With 153 eligible studies, 6 studies met the inclusion criteria for a subsequent meta-analysis, accounting for <a>273 healthy subjects and 313 patients </a>with symptomatic knee OA. The classification performance of supervised ML classifiers (OSH- or ANN-based) used in these studies was quantitatively assessed and compared across four following performance metrics: classification accuracy on the test set (ACC), sensitivity (SN), specificity (SP), and area under the receiver operating characteristic curve (AUC). Results There was no statistically significant discrepancy in the ACC between OSH- and ANN-based classifiers when dealing with kinetic and kinematic data concurrently, as well as when considering only kinematic data. However, there was a statistically significant difference in their SN and SP, with the ANN-based classifiers having higher SN and SP than OSH-based algorithms. As only one of the eligible studies reported AUC, this metric could not be assessed statistically across studies. Significance This study supports the use of ANN-based algorithms for classifying knee OA-related gait patterns as having a higher sensitivity and specificity than OSH-based classifiers. Considering their higher reliability, leveraging supervised ANN-based methods can aid biomechanists to diagnose knee OA objectively.

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