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

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.

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

2019 ◽  
Vol 9 (9) ◽  
pp. 1741 ◽  
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.

Pain perception in chronic knee osteoarthritis with varying levels of pain inhibitory control: an exploratory study

2020 ◽  
Vol 20 (4) ◽  
pp. 651-661
Author(s):  
Paulo E. P. Teixeira ◽  
Hanan I. Zehry ◽  
Swapnali Chaudhari ◽  
Laura Dipietro ◽  
Felipe Fregni
Keyword(s):  
Knee Osteoarthritis ◽  
Pain Intensity ◽  
Pain Perception ◽  
Small Sample Size ◽  
Linear Regression Analysis ◽  
Small Sample ◽  
Pain Modulation ◽  
Clinical Variables ◽  
Pain Scores ◽  
Intensity Perception

AbstractBackground and aimsPain is a disabling symptom in knee osteoarthritis (KOA) and its underlying mechanism remains poorly understood. Dysfunction of descending pain modulatory pathways and reduced pain inhibition enhance pain facilitation in many chronic pain syndromes but do not fully explain pain levels in chronic musculoskeletal conditions. The objective of this study is to explore the association of clinical variables with pain intensity perception in KOA individuals with varying levels of Conditioned Pain Modulation (CPM) response.MethodsThis is a cross-sectional, exploratory analysis using baseline data of a randomized clinical trial investigating the effects of a non-invasive brain stimulation treatment on the perception of pain and functional limitations due to KOA. Sixty-three subjects with KOA were included in this study. Data on pain perception, mood perception, self-reported depression, physical function, quality of life, and quantitative sensory testing was collected. Multiple linear regression analysis was performed to explore the association between the clinical variables with pain perception for individuals with different levels of CPM response.ResultsFor KOA patients with limited CPM response, perception of limitations at work/other activities due to emotional problems and stress scores were statistically significantly associated with pain scores, F(2, 37) = 7.02, p < 0.01. R-squared = 0.275. For KOA patients with normal CPM response, general health perception scores were statistically significantly associated with pain scores, F(1, 21) = 5.60, p < 0.05. R-squared = 0.2104. Limitations of this study include methodology details, small sample size and study design characteristics.ConclusionsPain intensity perception is associated differently with clinical variables according to the individual CPM response. Mechanistic models to explain pain perception in these two subgroups of KOA subjects are discussed.

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

2021 ◽  
Vol 11 (2) ◽  
pp. 834
Author(s):  
Marwa Mezghani ◽  
Nicola Hagemeister ◽  
Youssef Ouakrim ◽  
Alix Cagnin ◽  
Alexandre Fuentes ◽  
...  
Keyword(s):  
Physical Exercise ◽  
Knee Osteoarthritis ◽  
Decision Trees ◽  
Exercise Program ◽  
Patient Specific ◽  
Pain Level ◽  
Knee Kinematic ◽  
Kinematic Data ◽  
3D Kinematics ◽  
The Impact

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.

scholarly journals Comparison of statistical methods for analysis of small sample sizes for detecting the differences in efficacy between treatments for knee osteoarthritis

2020 ◽  
Author(s):  
Chia-Lung Shih ◽  
Te-Yu Hung
Keyword(s):  
Knee Osteoarthritis ◽  
Sample Size ◽  
Statistical Methods ◽  
Statistical Power ◽  
Permutation Test ◽  
Small Sample Size ◽  
Small Sample ◽  
T Test ◽  
Sample Sizes ◽  
Knee Oa

Abstract Background A small sample size (n < 30 for each treatment group) is usually enrolled to investigate the differences in efficacy between treatments for knee osteoarthritis (OA). The objective of this study was to use simulation for comparing the power of four statistical methods for analysis of small sample size for detecting the differences in efficacy between two treatments for knee OA. Methods A total of 10,000 replicates of 5 sample sizes (n=10, 15, 20, 25, and 30 for each group) were generated based on the previous reported measures of treatment efficacy. Four statistical methods were used to compare the differences in efficacy between treatments, including the two-sample t-test (t-test), the Mann-Whitney U-test (M-W test), the Kolmogorov-Smirnov test (K-S test), and the permutation test (perm-test). Results The bias of simulated parameter means showed a decreased trend with sample size but the CV% of simulated parameter means varied with sample sizes for all parameters. For the largest sample size (n=30), the CV% could achieve a small level (<20%) for almost all parameters but the bias could not. Among the non-parametric tests for analysis of small sample size, the perm-test had the highest statistical power, and its false positive rate was not affected by sample size. However, the power of the perm-test could not achieve a high value (80%) even using the largest sample size (n=30). Conclusion The perm-test is suggested for analysis of small sample size to compare the differences in efficacy between two treatments for knee OA.

scholarly journals AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

2021 ◽  
Vol 7 ◽  
pp. e495
Author(s):  
Saleh Albahli ◽  
Hafiz Tayyab Rauf ◽  
Abdulelah Algosaibi ◽  
Valentina Emilia Balas
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Synthetic Data ◽  
X Rays ◽  
Deep Convolutional Neural Networks ◽  
Current State ◽  
Pathology Classification ◽  
Wide Range ◽  
Multi Class Classification

Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

scholarly journals The Use of Artificial Intelligence in the Evaluation of Knee Pathology

2020 ◽  
Vol 24 (01) ◽  
pp. 021-029 ◽  
Author(s):  
Elisabeth R. Garwood ◽  
Ryan Tai ◽  
Ganesh Joshi ◽  
George J. Watts V
Keyword(s):  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Knee Osteoarthritis ◽  
Bone Tumors ◽  
Quantitative Assessment ◽  
Tendon Healing ◽  
Meniscal Tears ◽  
Resonance Imaging ◽  
Knee Pathology

AbstractArtificial intelligence (AI) holds the potential to revolutionize the field of radiology by increasing the efficiency and accuracy of both interpretive and noninterpretive tasks. We have only just begun to explore AI applications in the diagnostic evaluation of knee pathology. Experimental algorithms have already been developed that can assess the severity of knee osteoarthritis from radiographs, detect and classify cartilage lesions, meniscal tears, and ligament tears on magnetic resonance imaging, provide automatic quantitative assessment of tendon healing, detect fractures on radiographs, and predict those at highest risk for recurrent bone tumors. This article reviews and summarizes the most current literature.

Independence Estimating Equations for Controlled Clinical Trials with Small Sample Sizes

2006 ◽  
Vol 45 (04) ◽  
pp. 430-434 ◽  
Author(s):  
G. Dahmen ◽  
A. Ziegler
Keyword(s):  
Clinical Trials ◽  
Estimating Equations ◽  
Small Sample ◽  
Repeated Measurements ◽  
Robust Estimator ◽  
Variance Estimator ◽  
Sample Sizes ◽  
Controlled Clinical Trials ◽  
Parallel Group ◽  
Small Sample Sizes

Summary Objectives: The application of independence estimating equations (IEE) for controlled clinical trials (CCTs) has recently been discussed, and recommendations for its use have been derived for testing hypotheses. The robust estimator of variance has been shown to be liberal for small sample sizes. Therefore a series of modifications has been proposed. In this paper we systematically compare confidence intervals (CIs) proposed in the literature for situations that are common in CCTs. Methods: Using Monte-Carlo simulation studies, we compared the coverage probabilities of CIs and non-convergence probabilities for the parameters of the mean structure for small samples using modifications of the variance estimator proposed by Mancl and de Rouen [7], Morel et al. [8] and Pan [3]. Results: None of the proposed modifications behave well in each investigated situation. For parallel group designs with repeated measurements and binary response the method proposed by Pan maintains the nominal level. We observed non-convergence of the IEE algorithm in up to 10% of the replicates depending on response probabilities in the treatment groups. For comparing slopes with continuous responses, the approach of Morel et al. can be recommended. Conclusions: Results of non-convergence probabilities show that IEE should not be used in parallel group designs with binary endpoints and response probabilities close to 0 or 1. Modifications of the robust variance estimator should be used for sample sizes up to 100 clusters for CI estimation.

scholarly journals Refining Land-Cover Maps Based on Probabilistic Re-Classification in CCA Ordination Space

2020 ◽  
Vol 12 (18) ◽  
pp. 2954
Author(s):  
Yue Wan ◽  
Jingxiong Zhang ◽  
Wenjing Yang ◽  
Yunwei Tang
Keyword(s):  
Logistic Regression ◽  
Land Cover ◽  
Reference Data ◽  
Reference Sample ◽  
Training Sample ◽  
Small Sample ◽  
Cca Ordination ◽  
Sample Data ◽  
Map Data ◽  
Land Cover Maps

Due to spatial inhomogeneity of land-cover types and spectral confusions among them, land-cover maps suffer from misclassification errors. While much research has focused on improving image classification by re-processing source images with more advanced algorithms and/or using images of finer resolution, there is rarely any systematic work on re-processing existing maps to increase their accuracy. We propose refining existing maps to achieve accuracy gains by exploring and utilizing relationships between reference data, which are often already available or can be collected, and map data. For this, we make novel use of canonical correspondence analysis (CCA) to analyze reference-map class co-occurrences to facilitate probabilistic re-classification of map classes in CCA ordination space, a synthesized feature space constrained by map class occurrence patterns. Experiments using GlobeLand30 land-cover (2010) over Wuhan, China were carried out using reference sample data collected previously for accuracy assessment in the same area. Reference sample data were stratified by map classes and their spatial heterogeneity. To examine effects of model-training sample size on refinements, three subset samples (360, 720, and 1480 pixels) were selected from a pool of 3000 sample pixels (the full training sample). Logistic regression modeling was employed as a baseline method for comparisons. Performance evaluation was based on a test sample of 1020 pixels using a strict and relaxed definitions of agreement between reference classification and map classification, resulting in measures of types I and II, respectively. It was found that the CCA-based method is more accurate than logistic regression in general. With increasing sample sizes, refinements generally lead to greater accuracy gains. Heterogeneous sub-strata usually see greater accuracy gains than in homogeneous sub-strata. It was also revealed that accuracy gains in specific strata (map classes and sub-strata) are related to strata refinability. Regarding CCA-based refinements, a relatively small sample of 360 pixels achieved a 3% gain in both overall accuracy (OA) and F0.01 score (II). By using a selective strategy in which only refinable strata of cultivated land and forest are included in refinement, accuracy gains are further increased, with 5–11% gains in users’ accuracies (UAs) (II) and 4–10% gains in F0.01 scores (II). In conclusion, on condition of refinability, map refinement is well worth pursuing, as it increases accuracy of existing maps, extends utility of reference data, facilitates uncertainty-informed map representation, and enhances our understanding about relationships between reference data and map data and about their synthesis.

scholarly journals Knee kinematic curve representation and application to knee pathology classification

2018 ◽  
Vol 4 (1) ◽  
pp. 32 ◽  
Author(s):  
Badreddine Ben Nouma ◽  
Amar Mitiche ◽  
Youssef Ouakrim ◽  
Neila Mezghani
Keyword(s):  
Variational Method ◽  
Support Vector ◽  
Single Individual ◽  
Knee Kinematic ◽  
Classification Problems ◽  
Essential Information ◽  
Pathology Classification ◽  
Curve Representation ◽  
Knee Pathology ◽  
Corrected Curve

This study investigates a variational method to determine the most representative shape of a set of knee kinematic curves with application to knee pathology classification. Although they provide essential information for pathology classification, knee kinematic curves are characterized by high intra-class variability and outliers are often present. As a result, a set of several measurement curves are acquired of any single individual which are then averaged before their use for pathology classification. Rather than using the average of an individual’s recorded measurement curves, this method determines a better representative curve by first correcting the data to account for outliers occurrence and class variability using a variational method. The correction is performed by simultaneous minimization of a set of objective functions, one for each curve in the measurement set, and consisting of a weighed sum of two terms: a data term of conformity of the corrected curve to the given curve, and a regularization term of proximity of the corrected curve to the mean of all the corrected curves to inhibit the influence of outliers in the set. Validation tests were performed to discriminate between knee osteoarthritis data (OA) and non-OA data. Using a support vector machine, the classification accuracy with the proposed representation was 86%, with 81% sensitivity and 90% specificity, compared to 83% accuracy for the standard representation by average, with 76% sensitivity and 90% specificity. The representation has also been tested within the OA category to distinguish the femero-tibial patholgy from the femero-patellar, giving 76% accuracy, with 76% sensitivity and 76% specificity, compared to 69% accuracy, with 62% sensitivity and 76% specificity. These significant improvements by the proposed method warrant its further investigation by application to other biomedical engineering pattern classification problems and datasets.

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