Early Detection Of Knee Osteoarthritis Research Articles

Enabling early detection of knee osteoarthritis using diffusion–relaxation correlation spectrum imaging

To present a technique that enables detection of early stage OA of the knee using diffusion-relaxation correlation spectrum imaging (DR-CSI).Fifty-five early osteoarthritis patients (OA, Kellgren-Lawrence [KL] score 1 to 2; mean age, 56.4 years) and 49 healthy volunteers (mean age, 56.7 years) were underwent magnetic resonance imaging (MRI) with T2-mapping and DR-CSI techniques. Maps of mean apparent diffusion coefficient (ADC), T2 relaxation time and volume fraction Vi for DR-CSI compartment i (A, B, C, D) sensitivity, specificity, and positive and negative likelihood ratio (PLR, NLR) were assessed to determine the diagnostic accuracy for detection of early-stage degeneration of knee articular cartilage. The structural abnormalities of articular cartilage were evaluated using modified Whole-Organ MR Imaging Scores (WORMS).All intra- and interobserver agreements for DR-CSI compartment volume fractions and modified WORMS of cartilage were excellent. Early OA versus the controls had higher VC, lower VA and VB (p<0.001), but comparable VD (p>0.05). VA, VB and VC had a moderate association with WORMS. No significant correlation was identified between VD and WORMS. VC had better ability than VA,VB, VD, T2 and ADC to discriminate early OA patients from healthy controls (area under the curve, 0.898). Sensitivity, specificity, PLR, and NLR of VC with a cut-off value of 29.9% were 81.8% (95% confidence interval [CI], 69.1-90.9%), 95.9% (86-99.5%), 20.05% (5.13-78.34%), and 0.19% (0.11-0.33%).DR-CSI compartment volume fractions may be sensitive indicators for detecting early-stage degeneration in knee articular cartilage.

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach.

Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%.

Deep Convolutional Neural Network Classifier for Effective Knee Osteoarthritis Classification

Millions of people are affected by the disease Knee Osteoarthritis, and the prevalence of the condition is steadily increasing. Knee osteoarthritis has a significant impact on people's lives by generating increased worry, mental health disorders, and physical problems. Early detection of knee osteoarthritis is critical for decreasing disease consequences, and numerous studies are being conducted to classify knee osteoarthritis. In this study, the deep CNN classifier is used to classify knee osteoarthritis, which effectively extracts the features required for disease classification more efficiently. The preprocessing of the data, which is done in three processes such as Circular Fourier Transform, Multivariate Linear Function, and Histogram Equalization, is particularly important in this research since it aids in obtaining more efficient information about the image. The deep CNN classifier's weights and bias deliver better and desired classification results while spending less time and storage. The proposed deep CNN classifier attained the Accuracy of 94.244%, F1 measure of 94.059%, Precision of 94.059%, Recall of 93.586%.

Use of Machine Learning for Early Detection of Knee Osteoarthritis and Quantifying Effectiveness of Treatment Using Force Platform

Knee osteoarthritis is one of the most prevalent chronic diseases. It leads to pain, stiffness, decreased participation in activities of daily living and problems with balance recognition. Force platforms have been one of the tools used to analyse balance in patients. However, identification in early stages and assessing the severity of osteoarthritis using parameters derived from a force plate are yet unexplored to the best of our knowledge. Combining artificial intelligence with medical knowledge can provide a faster and more accurate diagnosis. The aim of our study is to present a novel algorithm to classify the occurrence and severity of knee osteoarthritis based on the parameters derived from a force plate. Forty-four sway movements graphs were measured. The different machine learning algorithms, such as K-Nearest Neighbours, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Decision Tree Classifier and Random Forest Classifier, were implemented on the dataset. The proposed method achieves 91% accuracy in detecting sway variation and would help the rehabilitation specialist to objectively identify the patient’s condition in the initial stage and educate the patient about disease progression.

Functional manifestations of early knee osteoarthritis: a systematic review and meta-analysis.

Early detection of knee osteoarthritis (KOA) can improve treatment outcomes and prevent its progression. The aim of this systematic review was to identify the functional changes in early KOA. Electronic journal databases and platforms, including PubMed, the Physiotherapy Evidence Database, the Cochrane Central Register of Controlled Trials, and Scopus were searched. The inclusion criteria were as follows: (1) studies comparing patients with early KOA with an age-matched control group and (2) studies with objectively measured functional changes as outcomes. Studies that included individuals with Kellgren and Lawrence (K/L) grades > 2- were excluded. A random-effects model was constructed to calculate pooled standardized mean differences (SMDs). A total of nine articles were included in this systematic review. Seven studies used classification criteria to define early KOA, including knee pain; a K/L grade of 0, 1, or 2- (osteophytes only) for the medial compartment; and the presence of two out of four MRI criteria. The remaining two studies included K/L grade 1 confirmed by radiography. Early KOA participants had a significantly longer timed up-and-go test (TUG) time (pooled SMD: 0.57; 95% confidence interval: 0.15, 0.98). The two groups had similar knee extension muscle strength at 90° knee flexion. The quality of evidence for each measured outcome was "very low." In this review, longer TUG was identified as a functional manifestation of early KOA. Further studies involving functional assessments are needed to develop a screening method to detect early KOA. Key Points • There is a need for diagnostic criteria that include functional changes in patients with early knee osteoarthritis, since radiographic facilities are not available everywhere. • In this review, a long timed up-and-go test time was identified as a functional manifestation of early knee osteoarthritis. • If the findings of this study can be replicated, measurement of TUG may allow for earlier detection of knee osteoarthritis outside the hospital and in routine clinical practice without the use of MRI or X-rays.

A clinical test examination procedure to identify knee compartment overloading: A reliability and validity study using SPECT-CT as reference

BackgroundMechanical forces and joint misalignment are considered risk factors for the development of knee osteoarthritis (KOA). Early detection of KOA and distinction between lateral and medial compartment overloading (CO), might be important to inform appropriate preventative interventions. This study evaluated reliability and validity of a test battery consisting of ten clinical tests to predict knee CO. MethodsIndependent observers examined 30 participants with symptoms of KOA. Inter-rater reliability of the ten tests, as well as the anticipated CO based on the whole test battery, was determined. All participants received a SPECT-CT, which served as reference standard for CO. The agreement for CO between SPECT-CT and clinical examination was assessed to determine criterion validity. ResultsThe Kappa coefficients (k) for the ten individual clinical tests ranged from 0.19 to 0.80. The k for determining CO was 0.52 (95% CI = 0.28–0.76). The agreement for CO between SPECT-CT and clinical examination (i.e. criterion validity) yielded a k of 0.26 (95% CI = −0.06 - 0.58). Logistic regression indicated that valgus alignment was strongly related with lateral CO. No other relationships were found between individual tests and CO. ConclusionAccurate measurement of frontal plane knee angle is important to determine CO. This particular test yielded good reliability, but low validity. Reliability of the nine remaining clinical tests was fair to moderate. Criterion validity of the clinical examination to predict CO was low. Therefore, this test battery in its current form cannot be used in practice to determine CO.

Discriminative Regularized Auto-Encoder for Early Detection of Knee OsteoArthritis: Data from the Osteoarthritis Initiative.

OsteoArthritis (OA) is the most common disorder of the musculoskeletal system and the major cause of reduced mobility among seniors. The visual evaluation of OA still suffers from subjectivity. Recently, Computer-Aided Diagnosis (CAD) systems based on learning methods showed potential for improving knee OA diagnostic accuracy. However, learning discriminative properties can be a challenging task, particularly when dealing with complex data such as X-ray images, typically used for knee OA diagnosis. In this paper, we introduce a Discriminative Regularized Auto Encoder (DRAE) that allows to learn both relevant and discriminative properties that improve the classification performance. More specifically, a penalty term, called discriminative loss is combined with the standard Auto-Encoder training criterion. This additional term aims to force the learned representation to contain discriminative information. Our experimental results on data from the public multicenter OsteoArthritis Initiative (OAI) show that the developed method presents potential results for early knee OA detection.

Lipid profiles in Hoffa’s fat pad as biomarker for cartilage regeneration and osteoarthritis development

Purpose: Early detection of knee osteoarthritis (OA) is critical for possible preventive treatment. Specific biomarkers for prognosis and early diagnosis of OA are lacking. Therefore, in this study, we analyzed the lipid profiles of different tissue types within Hoffa’s fat pad (HFP) of OA and cartilage defect (CD) patients, using matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI). The HFP has already been shown to play an important role in the inflammatory process in OA by prostaglandin release.

Stair climbing ability in patients with early knee osteoarthritis: Defining the clinical hallmarks of early disease

BackgroundA growing clinical interest has been shown towards identifying knee osteoarthritis (OA) patients at earlier stages. The early detection of knee OA may allow for more effective interventions. Research questionThe aim of this study was to determine the discriminative ability of a stair-climb test (SCT) in identifying patients with early knee OA, and to determine if descending stair time during the SCT is better than ascending stair time for the identification of these patients. MethodsThis study was a secondary, cross-sectional analysis of baseline data from a randomized controlled trial. Adults with moderate to severe knee pain were enrolled (n = 57; mean age 58.9 years; 71.9% women). Each participant performed an 11-step SCT (11-SCT) while wearing shoes with a pressure sensor insole. A receiver operating characteristic analysis was used to examine the discriminative power of 11-SCT for identifying early knee OA (Kellgren and Lawrence grade 1). The discriminative power was also compared between the ascending and descending 11-SCT time as evaluated by the pressure sensor. ResultsThe 11-SCT time in patients with early knee OA was 0.55 s longer than that in those with symptomatic non-radiographic OA. A one-second increase in the 11-SCT time was significantly associated with 1.9-fold increased odds of early knee OA being present. The 11-SCT value with the best balance of sensitivity and specificity for identifying early knee OA was 8.33 s (area under the curve: 0.711). The descending time was not significantly better than the ascending time for identifying early knee OA. SignificanceThis study determined the time values of an 11-SCT that may be useful for identifying early knee OA patients. These preliminary findings may serve as the foundation for future studies investigating the clinical hallmarks associated with early knee OA.

A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative

This paper presents a fully developed computer aided diagnosis (CAD) system for early knee OsteoArthritis (OA) detection using knee X-ray imaging and machine learning algorithms. The X-ray images are first preprocessed in the Fourier domain using a circular Fourier filter. Then, a novel normalization method based on predictive modeling using multivariate linear regression (MLR) is applied to the data in order to reduce the variability between OA and healthy subjects. At the feature selection/extraction stage, an independent component analysis (ICA) approach is used in order to reduce the dimensionality. Finally, Naive Bayes and random forest classifiers are used for the classification task. This novel image-based approach is applied on 1024 knee X-ray images from the public database OsteoArthritis Initiative (OAI). The results show that the proposed system has a good predictive classification rate for OA detection (82.98% for accuracy, 87.15% for sensitivity and up to 80.65% for specificity).

Vibroarthrography for early detection of knee osteoarthritis using normalized frequency features.

Vibroarthrography is a radiation-free and inexpensive method of assessing the condition of knee cartilage damage during extension-flexion movements. Acoustic sensors were placed on the patella and medial tibial plateau (two accelerometers) as well as on the lateral tibial plateau (a piezoelectric disk) to measure the structure-borne noise in 59 asymptomatic knees and 40 knees with osteoarthritis. After semi-automatic segmentation of the acoustic signals, frequency features were generated for the extension as well as the flexion phase. We propose simple and robust features based on relative high-frequency components. The normalized nature of these frequency features makes them insusceptible to influences on the signal gain, such as attenuation by fat tissue and variance in acoustic coupling. We analyzed their ability to serve as classification features for detection of knee osteoarthritis, including the effect of normalization and the effect of combining frequency features of all three sensors. The features permitted a distinction between asymptomatic and non-healthy knees. Using machine learning with a linear support vector machine, a classification specificity of approximately 0.8 at a sensitivity of 0.75 could be achieved. This classification performance is comparable to existing diagnostic tests and hence qualifies vibroarthrography as an additional diagnostic tool. Graphical Abstract Acoustic frequency features were used to detect knee osteoarthritis at 80% specificity and 75% sensitivity.

Ультразвуковая сонография и магнитно-резонансная томография в выявлении дегенеративных изменений при рентгенологически нулевой стадии остеоартроза коленных суставов

Osteoarthritis is a group of different diseases with similar manifestations, which are based on the defeat of all of the joint parts (primarily of cartilage and subchondral bone, synovium, ligaments capsule) and periarticular muscles. The purpose of the study is to characterize cartilage structure and periarticular changes of tissue by means of ultrasound and magnetic resonance imaging without X-ray verification of osteoarthritis. Thirty eight patients with X-ray negative stage of osteoarthritis were ex-amined by ultrasound and magnetic resonance imaging. Pathological changes were detected in majority of the patients. The edema of soft tissue, exudate, changes in ligaments, deformation of joint surface were revealed in 71-74%, 11-37%, 76-68% and 58-68% of patients respectively. In general, pathological changes tended to be revealed on magnetic resonance imaging more frequently comparatively to ultrasound examination. Both ultrasound examination and magnetic resonance imaging are comprehensive methods for early detection of knee osteoarthritis. The prognostic value of the changes is to be evaluated.

Contrast enhancement of ultrasound imaging of the knee joint cartilage for early detection of knee osteoarthritis

Knee osteoarthritis (OA) is one of the most common diseases among the elderly people. Typically, medical attention is not sought until the disease has progressed to a point at which it is not possible to treat effectively, often due to concerns over the cost of detection at an earlier stage. Ultrasound (US) imaging has a number of advantages as an imaging technique; apart from being a low cost diagnostic method, it is also non-invasive, utilizes non-ionizing radiation and portable. Due to progression of knee OA, the cartilage will experience a significant change in shape, and it becomes degenerated. After image processing using US medical imaging, it is possible to detect the cartilage shape change of the knee joint. Low contrast ratio and speckle noise are two main disadvantages of US imaging. The aim of this paper is to present a method for enhancing the contrast of the US image of knee joint cartilage in detecting early stages of knee OA. Conventional contrast enhancing methods are known to have some limitations. The objective of this paper is to propose a new contrast enhancing method which can overcome the limitations of the conventional contrast enhancing method. Most conventional contrasts enhancing methods emphasize only on one character. In the proposed method, the optimum value of contrast, brightness and detail preservation are considered. The proposed method is applied to find out the optimum separating point for segmenting the histogram of US image, for which optimum value of contrast, brightness and detail preservation will be preserved. In this method as well, three metrics, named as Preservation of Brightness Score function (PBS), Optimum Contrast Score function (OCS) and Preservation of Detail Score function (PDS), are defined.

Non-invasive and in vivo assessment of osteoarthritic articular cartilage: a review on MRI investigations.

Early detection of knee osteoarthritis (OA) is of great interest to orthopaedic surgeons, rheumatologists, radiologists, and researchers because it would allow physicians to provide patients with treatments and advice to slow the onset or progression of the disease. Early detection can be achieved by identifying early changes in selected features of degenerative articular cartilage (AC) using non-invasive imaging modalities. Magnetic resonance imaging (MRI) is becoming the standard for assessment of OA. The aim of this paper was to review the influence of MRI on the selection, detection, and measurement of AC features associated with early OA. Our review of the literature indicates that the changes associated with early OA are in cartilage thickness, cartilage volume, cartilage water content, and proteoglycan content that can be accurately, consistently, and non-invasively measured using MRI. Choosing an MR pulse sequence that provides the capability to assess cartilage physiology and morphology in a single acquisition and advanced multi-nuclei MRI is desirable. The results of the review indicate that using an ultra-high magnetic strength, MR imager does not affect early OA detection. In conclusion, MRI is currently the most suitable modality for early detection of knee OA, and future research should focus on the quantitative evaluation of early OA features using advances in MR hardware, software, and data processing with sophisticated image/pattern recognition techniques.

Crepitus is a first indication of patellofemoral osteoarthritis (and not of tibiofemoral osteoarthritis)

The patellofemoral joint (PFJ) is important in early detection of knee osteoarthritis (OA). Little is known about the relationship between specific clinical findings and PFJ Magnetic resonance Imaging (MRI) features. The objective was to examine the relationship between (early) clinical findings and PFJ MRI features in females (45-60 years) without knee OA (PFJ or tibiofemoral joint (TFJ) OA) based on a recently suggested MRI definition. MRIs of knees of women of a sub-study of the Rotterdam Study were scored with semi-quantitative scoring. Specific patellar tests were performed on physical examination. Current knee pain and history of patellar knee pain were reported. Binomial logistic generalized estimated equations were used to determine the association between clinical findings of OA and PFJ MRI features. All associations were adjusted for age, body mass index (BMI) and TFJ MRI features. In 888 women (1776 knees, mean age: 55.1 years and mean BMI: 27.0kg/m(2)) we found significant associations between crepitus and all PFJ MRI features (Odds ratios (OR) range: 2.61-5.49). A history of patellar pain was significantly associated with almost all PFJ MRI features (ORcartilage: 1.95; ORcysts: 1.86; ORbone marrow lesions: 1.83), except for osteophytes. No significant associations were found between the clinical findings and TFJ MRI features. Crepitus and history of patellar pain are clinical findings that indicate PFJ lesions seen on MRI. These tests could help to indicate signs of PFJOA. Follow-up data needs to confirm whether these tests have an additional diagnostic value for early knee OA in PFJ or TFJ.

Urinary concentrations of type II collagen cleavage following anterior cruciate ligament reconstruction

Urinary concentrations of type II collagen cleavage following anterior cruciate ligament reconstruction

Osteoarthritic Cartilage Is More Homogeneous Than Healthy Cartilage: Identification of a Superior Region of Interest Colocalized With a Major Risk Factor for Osteoarthritis

Cartilage loss as determined by magnetic resonance imaging (MRI) or joint space narrowing as determined by x-ray is the result of cartilage erosion. However, metabolic processes within the cartilage that later result in cartilage loss may be a more sensitive assessment method for early changes. Recently, it was shown that cartilage homogeneity visualized by MRI representing the biochemical changes undergoing in the cartilage is a potential marker for early detection of knee osteoarthritis (OA) and is also able to significantly separate groups of healthy subjects from those with OA. The purpose of this study was twofold. First, we wished to evaluate whether the results on cartilage homogeneity from the previous study can be reproduced using an independent population. Second, based on the homogeneity framework, we present an automatic technique that partitions the region of interest in the cartilage that contributes most to discrimination between healthy and OA subjects and allows for identification of the most implicated areas in early OA. These findings may allow further investigation of whether cartilage homogeneity reveals a predisposition for OA or whether it evolves as a consequence to disease and thereby can be used as a progression biomarker.A total of 283 right and left knees from 159 subjects aged 21 to 81 years were scanned using a Turbo 3D T1 sequence on a 0.18-T MRI Esaote scanner. The medial compartment of the tibial cartilage sheet was segmented using a fully automatic voxel classification scheme based on supervised learning. From the segmented cartilage sheet, homogeneity was quantified by measuring entropy from the distribution of signal intensities inside the compartment. Each knee was examined by radiography, and the knees were categorized by the Kellgren and Lawrence (KL) Index. Next, based on a gradient descent optimization technique, the cartilage region that contributed to the maximum statistical significance of homogeneity in separating healthy subjects from the diseased was partitioned. The generalizability of the region was evaluated by testing for overfitting. Three different regularization techniques were evaluated for reducing overfitting errors.The P values for separating the different groups based on cartilage homogeneity were 2 x 10(-5) (KL 0 versus KL 1) and 1 x 10(-7) (KL 0 versus KL >0). Using the automatic gradient descent technique, the partitioned region was toward the peripheral part of the cartilage sheet. Using this region, the P values for separating the different groups based on homogeneity were 5 x 10(-9) (KL 0 versus KL 1) and 1 x 10(-15) (KL 0 versus KL >0). The precision of homogeneity for the partitioned region assessed as a test-retest root-mean-square coefficient of variation was 3.3%. Bootstrapping proved to be an effective regularization tool in reducing overfitting errors.The validation study supported the use of cartilage homogeneity as a tool for the early detection of knee OA and for separating groups of healthy subjects from those who have disease. Our automatic, unbiased partitioning algorithm based on a general statistical framework outlined the cartilage region of interest that best separated healthy from OA conditions on the basis of homogeneity discrimination. We have shown that OA affects certain areas of the cartilage more distinctly, and these areas are located more toward the peripheral region of the cartilage. We propose that this region corresponds anatomically to cartilage covered by the meniscus in healthy subjects. This finding may provide valuable clues in the early detection and monitoring of OA and thus may improve treatment efficacy.

Clinical value of 99mTc-methylene diphosphonate (MDP) bone single photon emission computed tomography (SPECT) in patients with knee osteoarthritis

To evaluate that single photon emission computed tomography (SPECT) could reflect the clinical severity of knee osteoarthritis (OA) and to determine the clinical usefulness of SPECT as a sensitive imaging method detecting early OA. Sixty knees of thirty OA patients were examined with (99m)Tc-methylene diphosphonate (MDP) bone SPECT, the intensity of uptake was graded and the sites of uptake were localized. One hundred millimeter visual analog scale (VAS), Western Ontario McMaster Universities (WOMAC) Osteoarthritis Index scores and physical examination were assessed. Simple radiograph and musculoskeletal ultrasonography (US) were performed, and Kellgren-Lawrence (K-L) grades were determined. The relationships between clinical findings, simple radiographic findings, US findings and the intensity of uptake in SPECT were analyzed. There were increased uptakes in 91.7% of the knees, and patella and medial tibial condyle were the most common sites of the uptakes. Symptomatic knees had significantly higher intensity of uptake than asymptomatic knees. Uptake intensity of medial compartment in SPECT well correlated with clinical findings, such as VAS, WOMAC scores, and physical examination, as well as US findings. Even in the symptomatic knees without abnormal radiographic findings, the increased uptake was observed in SPECT. In patients with knee OA, SPECT findings are well correlated with clinical findings, such as pain scores and physical examinations, and SPECT appears to be a sensitive tool for early detection of knee OA. SPECT information could be useful for determining clinical severity of knee OA and for diagnosing early OA more effectively.

Separation of healthy and early osteoarthritis by automatic quantification of cartilage homogeneity

Cartilage loss as determined either by magnetic resonance imaging (MRI) or by joint space narrowing in X-rays is the result of cartilage erosion. However, metabolic processes within the cartilage that later result in cartilage loss may be a more accurate assessment method for early changes. Early biological processes of cartilage destruction are among other things, a combination of proteoglycan turnover, as a result of altered charge distributions, and local alterations in water content (edema). As water distribution is detectable by MRI, the aim of this study was to investigate cartilage homogeneity visualized by MRI related to water distribution, as a potential very early marker for early detection of knee osteoarthritis (OA). One hundred and fourteen right and left knees from 71 subjects aged 22-79 years were scanned using a Turbo 3D T(1) sequence on a 0.18T MRI Esaote scanner. The medial compartment of the tibial cartilage sheet was segmented using a fully automatic voxel classification scheme based on supervised learning. From the segmented cartilage sheet, homogeneity was quantified by measuring entropy from the distribution of signal intensities inside the compartment. For each knee an X-ray was acquired and the knees were categorized by the Kellgren and Lawrence (KL) index and the joint space width (JSW) was measured. The P-values for separating the groups by each of JSW, cartilage volume, cartilage mean intensity, and cartilage homogeneity were calculated using the unpaired t-test. The P-value for separating the group diagnosed as KL 0 from the group being KL 1 based on JSW, volume and mean signal intensity the values were P=0.9, P=0.4 and P=0.0009, respectively. In contrast, the P-value for homogeneity was P=0.0004. The precision of the measures assessed, as a test-retest root mean square coefficient of variation (RMS-CV%) was 3.9% for JSW, 7.4% for volume, 3.9% for mean signal intensity and 3.0% for homogeneity quantification. These data demonstrate that the distribution of components of the articular matrix precedes erosion, as measured by cartilage homogeneity related to water concentration. We show that homogeneity was able to separate early OA from healthy individuals in contrast to traditional volume and JSW quantifications. These data suggest that cartilage homogeneity quantification may be able to quantify early biochemical changes in articular cartilage prior to cartilage loss and thereby provide better identification of patients for OA trials who may respond better to medicinal intervention of some treatments. In addition, this study supports the feasibility of using low-field MRI in clinical studies.