Early Diagnosis Of Knee Osteoarthritis Research Articles

Construction of multi-scale feature fusion segmentation model of MRI knee images based on dual attention mechanism weighted aggregation.

Early diagnosis of knee osteoarthritis is an important area of research in the field of clinical medicine. Due to the complexity in the MRI imaging sequences and the diverse structure of cartilage, there are many challenges in the segmentation of knee bone and cartilage. Relevant studies have conducted semantic fusion processing through splicing or summing forms, which results in reduced resolution and the accumulation of redundant information. This study was envisaged to construct an MRI image segmentation model to improve the diagnostic efficiency and accuracy of different grade knee osteoarthritis by adopting the Dual Attention and Multi-scale Feature Fusion Segmentation network (DA-MFFSnet). The feature information of different scales was fused through the Multi-scale Attention Downsample module to extract more accurate feature information, and the Global Attention Upsample module weighted lower-level feature information to reduce the loss of key information. The collected MRI knee images were screened and labeled, and the study results showed that the segmentation effect of DA-MFFSNet model was closer to that of the manually labeled images. The mean intersection over union, the dice similarity coefficient and the volumetric overlap error was 92.74%, 91.08% and 7.44%, respectively, and the accuracy of the differential diagnosis of knee osteoarthritis was 84.42%. The model exhibited better stability and classification effect. Our results indicated that the Dual Attention and Multi-scale Feature Fusion Segmentation model can improve the segmentation effect of MRI knee images in mild and medium knee osteoarthritis, thereby offering an important clinical value and improving the accuracy of the clinical diagnosis.

Development of an automated optimal distance feature-based decision system for diagnosing knee osteoarthritis using segmented X-ray images

Knee Osteoarthritis (KOA) is a leading cause of disability and physical inactivity. It is a degenerative joint disease that affects the cartilage, cushions the bones, and protects them from rubbing against each other during motion. If not treated early, it may lead to knee replacement. In this regard, early diagnosis of KOA is necessary for better treatment. Nevertheless, manual KOA detection is time-consuming and error-prone for large data hubs. In contrast, an automated detection system aids the specialist in diagnosing KOA grades accurately and quickly. So, the main objective of this study is to create an automated decision system that can analyze KOA and classify the severity grades, utilizing the extracted features from segmented X-ray images. In this study, two different datasets were collected from the Mendeley and Kaggle database and combined to generate a large data hub containing five classes: Grade 0 (Healthy), Grade 1 (Doubtful), Grade 2 (Minimal), Grade 3 (Moderate), and Grade 4 (Severe). Several image processing techniques were employed to segment the region of interest (ROI). These included Gradient-weighted Class Activation Mapping (Grad-Cam) to detect the ROI, cropping the ROI portion, applying histogram equalization (HE) to improve contrast, brightness, and image quality, and noise reduction (using Otsu thresholding, inverting the image, and morphological closing). Besides, the focus filtering method was utilized to eliminate unwanted images. Then, six feature sets (morphological, GLCM, statistical, texture, LBP, and proposed features) were generated from segmented ROIs. After evaluating the statistical significance of the features and selection methods, the optimal feature set (prominent six distance features) was selected, and five machine learning (ML) models were employed. Additionally, a decision-making strategy based on the six optimal features is proposed. The XGB model outperformed other models with a 99.46 % accuracy, using six distance features, and the proposed decision-making strategy was validated by testing 30 images.

Assessment of blood flow around the knee joint in patients with knee osteoarthritis by color Doppler ultrasound

ObjectiveTo explore a method for early diagnosis of knee osteoarthritis (OA) by comparing vascular morphology and hemodynamic parameters between healthy controls and patients, and explore the effects of age on the blood flow of healthy subjects. Material and methods80 healthy subjects and 106 patients with knee OA were recruited. Color Doppler ultrasound was used to measure the vascular morphology and hemodynamic parameters of 11 blood vessels around the knee joint. A general linear model was used to analyze the difference of blood flow parameters between healthy controls and patients, and to explore the effects of sex and age on the blood flow of healthy subjects. ResultsAge has no significant effect on the vessel diameter of healthy subjects, and it only affect the blood flow velocity (P ＜ 0.05) and blood flow volume (P ＜0.0083) of a few blood vessels. There was no significant difference in vascular morphological parameter between healthy controls, patients with mild OA and moderate/severe OA, but there was significant difference in vascular hemodynamic parameters (P ＜ 0.0167). Specifically, in the early stage of knee OA, hemodynamic parameters of main arteries in patients did not change significantly, but those of branch vessels changed significantly (P ＜ 0.0167). With the development of knee OA, the difference of hemodynamic parameters of branch vessels in patients increased gradually. ConclusionHemodynamic parameters of branch vessels around the knee joint have changed significantly in the early stage of knee OA, which can be treated as an important index to screening the early patients.

Retracted: Preliminary Study on Grading Diagnosis of Early Knee Osteoarthritis by Shear Wave Elastography.

[This retracts the article DOI: 10.1155/2022/4229181.].

A discriminative shape-texture convolutional neural network for early diagnosis of knee osteoarthritis from X-ray images.

Knee Osteoarthritis (OA) is one of the most common causes of physical disability worldwide associated with a significant personal and socioeconomic burden. Deep Learning approaches based on Convolutional Neural Networks (CNNs) achieved remarkable improvements in knee OA detection. Despite this success, the problem of early knee OA diagnosis from plain radiographs remains a challenging task. This is due to the high similarity between the X-ray images of OA and non-OA subjects and the disappearance of texture information regarding bone microarchitecture changes in the top layers during the learning process of the CNN models. To address these issues, we propose a Discriminative Shape-Texture Convolutional Neural Network (DST-CNN), which automatically diagnoses early knee OA from X-ray images. The proposed model incorporates a discriminative loss to improve class separability and deal with high inter-class similarities. In addition, a new Gram Matrix Descriptor (GMD) block is embedded in the CNN architecture to compute texture features from several intermediate layers and combine them with the shape features in the top layers. We show that merging texture features with deep ones leads to better prediction of the early stages of OA. Comprehensive experimental results on two large public databases, Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST) demonstrate the potential of the proposed network. Ablation studies and visualizations are provided for a detailed understanding of our proposed approach.

Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted.

Knee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, affecting more than 5% of people worldwide. KOA goes through many stages, from the mild grade that can be treated to the severe grade in which the knee must be replaced. Therefore, early diagnosis of KOA is essential to avoid its development to the advanced stages. X-rays are one of the vital techniques for the early detection of knee infections, which requires highly experienced doctors and radiologists to distinguish Kellgren-Lawrence (KL) grading. Thus, artificial intelligence techniques solve the shortcomings of manual diagnosis. This study developed three methodologies for the X-ray analysis of both the Osteoporosis Initiative (OAI) and Rani Channamma University (RCU) datasets for diagnosing KOA and discrimination between KL grades. In all methodologies, the Principal Component Analysis (PCA) algorithm was applied after the CNN models to delete the unimportant and redundant features and keep the essential features. The first methodology for analyzing x-rays and diagnosing the degree of knee inflammation uses the VGG-19 -FFNN and ResNet-101 -FFNN systems. The second methodology of X-ray analysis and diagnosis of KOA grade by Feed Forward Neural Network (FFNN) is based on the combined features of VGG-19 and ResNet-101 before and after PCA. The third methodology for X-ray analysis and diagnosis of KOA grade by FFNN is based on the fusion features of VGG-19 and handcrafted features, and fusion features of ResNet-101 and handcrafted features. For an OAI dataset with fusion features of VGG-19 and handcrafted features, FFNN obtained an AUC of 99.25%, an accuracy of 99.1%, a sensitivity of 98.81%, a specificity of 100%, and a precision of 98.24%. For the RCU dataset with the fusion features of VGG-19 and the handcrafted features, FFNN obtained an AUC of 99.07%, an accuracy of 98.20%, a sensitivity of 98.16%, a specificity of 99.73%, and a precision of 98.08%.

Automated knee osteoarthritis severity classification using three‐stage preprocessing method andVGG16architecture

AbstractAs a nutshell, an early diagnosis of Knee Osteoarthritis (KOA) enhances the likelihood of an individual receiving treatment at its onset and prevents joint replacement surgery. Computer‐Aided‐Diagnosis (CAD) from the radiograph images have gained a wide‐spread attention in automated grading of KOA severity. But radiograph images have certain limitations, such as presence of too much noise and uneven contrast distribution across the image that impacts the accuracy and reliability of CAD systems. Therefore, a novel three‐stage pre‐processing method has been proposed using a combination of different techniques applied at sequential stages such as noise‐reduction using gaussian‐filter, normalization using pixel‐centering method, and balanced contrast enhancement technique. A transfer‐learning based VGG16 architecture has been used for severity classification. Our classification framework outperforms the existing state‐of‐the‐art methods achieving an excellent accuracy of 89.95\%. Therefore, our system can be used by the radiologists and physicians as a decision‐support tool in their daily diagnosis procedure.

Screening Clinical Changes for the Diagnosis of Early Knee Osteoarthritis: A Cross-Sectional Observational Study.

Background: The main objective was to evaluate differences in the clinical, motor, or functional variables in patients with Early Osteoarthritis (EOA) and individuals at risk of developing osteoarthritis (OA). Methods: A cross-sectional study was performed. All the participants were divided into two groups: EOA patients and healthy subjects (HS) at risk of developing OA. The main outcomes were clinical tests, such as those of knee morphology, instability, or proprioception; motor and functional variables, such as knee strength, range of motion, walking speed, and the sit-to-stand test; pain and disability, assessed through the Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) and Knee injury and Osteoarthritis Outcome Score (KOOS) scales; and knee alignment and leg length inequality, assessed via X-ray images. Results: A total of 97 participants were included (54 EOA and 43 HS). Patients with EOA showed a greater presence of knee pain (p < 0.01). In addition, more EOA patients showed instability both in the left (p < 0.01) and right legs (p < 0.05). Regarding the knee alignment variable, significant differences were found (p < 0.04), with more patients with EOA diagnosed as possessing a varus alignment. In addition, EOA patients showed lower knee strength, since statistically significant differences were found regarding flexion and extension strength in the left leg (Mean Difference (MD): 12.92; p = 0.03; d = -0.46 and MD:7.81; p = 0.04; d = -0.39). Differences were found for the sit-to-stand test scores, showing lower results for the EOA group (MD: -1.91; p < 0.01; d = 0.54). Conclusions: The results of this research show statistically significant differences between patients with EOA and HS at risk of developing OA with respect to pain, disability, instability, knee strength, and the sit-to-stand test. Our results suggest that the evaluation of clinical, motor, and functional features could contribute to an early management of knee OA.

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network.

Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works.

Texture Analysis in MRI of the Knee for an Early Diagnosis of Osteoarthritis.

Osteoarthritis is one of the most disabling diseases in developed countries. Its etiology is not firmly established, and the diagnosis is made by observing radiographs, assigning a degree of severity based on the information displayed. For this reason, the diagnosis is usually late and determined by the subjectivity of the doctor, which implies a restriction of the treatment. Magnetic resonance imaging (MRI) has allowed us to see in greater detail the alterations produced in soft joint structures. In this work, biomarkers for an early diagnosis of knee osteoarthritis have been developed by means of textures analysis on MRI. For this purpose, 50 subjects underwent T1-weighted MR image acquisitions: 25 controls and 25 diagnosed with knee osteoarthritis between grades I and III. Six regions were segmented on these images, corresponding to the femorotibial cartilage, femoral condyles, and tibial plateau. 43 textures were extracted for each region of interest (ROI) employing 5 statistical methods and 5 different predictive models were trained and compared. In addition, a study of the thickness of the cartilage was carried out to make a comparison with the texture analysis. The best result has been obtained using a K-nearest neighbor model with the combination of 33 textures (maximum value of AUC = 0.7684). Furthermore, in the analysis of the cartilage thickness, no statistically significant differences were found. Finally, it is concluded that the texture analysis has great potential for the diagnosis of knee osteoarthritis. Clinical Relevance - The current study establishes a methodology for an early diagnosis of knee osteoarthritis by means of MRI-based texture analysis, in a fast and objective manner.

Effects of neuroplasticity in people with knee osteoarthritis: A systematic review of the literature.

Background:Knee osteoarthritis (OA) is associated with chronic inflammation in somatic structures, which alters sensory afferents and leads to plastic changes in the nervous system.Methods:A systematic literature review was carried out, without language restrictions, period, or status of publication. The database used were Medline, EMBASE, Cochrane Library and clinicaltrials.gov. Extra bibliographic references were extracted through the discussion with specialists, and through scientific researches in conference papers.Results:The electronic search found 938 articles. When excluding duplicates and applying the inclusion/exclusion criteria, 5 studies were considered: 2 using EEG and 3 using TMS. Significant reduction of EEG activity in the cingulate medium cortex, reduction of conditioned pain modulation (CPM) in studies with EEG, as well as the occurrence of an association between pain and motor response threshold/intracortical pain facilitation in studies with TMS were observed.Conclusions:The study contributes to a better understanding of the neurophysiological changes seen in the cingulate medium cortex, decrease in CPM and motor response threshold/intracortical pain facilitation. Advances in neuroplasticity studies may aid in the screening for early diagnosis of knee OA in the future. However, more studies are necessary.

Preliminary Study on Grading Diagnosis of Early Knee Osteoarthritis by Shear Wave Elastography.

Knee osteoarthritis (KOA) is the most common chronic bone joint disease. The WHO points out that KOA has become the fourth most disabling disease in the world, and the main clinical treatment is prevention. At present, the clinical diagnosis of knee osteoarthritis with deformation is mainly made by X-ray and two-dimensional ultrasound, and the preventive treatment effect is not good. Shear wave elastography (SWE) has been widely used in clinical practice for its advantages of noninvasive, simple, rapid, and high accuracy in soft tissue hardness. This study investigates the feasibility of using SWE in the grading diagnosis of KOA and the detection of early skeletal muscle injury. 60 patients were enrolled with unilateral KOA who met the inclusion criteria. Routine scanning and SWE exploration were carried out and compared between the affected and healthy knee joints. The measurements included morphology, damage degree of cartilage, joint effusion, joint synovial thickness, the degree of meniscus convexity, and shear wave speed (SWS) in the tendon attachment area of the quadriceps femoris superior patella, and tendon thickness and SWS between and within groups. The affected was scored according to the ultrasonic characteristics. The correlation between them was analyzed to evaluate the feasibility of SWE in grading the diagnosis of KOA. 57 cases had knee cavity effusion (57/60, 95.0%), 58 cases had knee cartilage injury (58/60, 96.6%), and 35 cases had a lateral process of the medial meniscus (35/60, 53.3%); 30 cases had synovial hyperplasia (30/60, 50%), 21 cases had osteophyte formation (21/60, 35%), and no one had a popliteal cyst. There was no significant difference in tendon thickness between and within groups (p > 0.05), but a significant difference in SWS (p < 0.01); the SWS of the quadriceps femoris tendon is negatively correlated with ultrasound score (r = 0.955, p < 0.01). There is a strong negative correlation between SWS and the degree of KOA. It can be used in the grading diagnosis of KOA to help find more early strain points.

Tenderness of the knee is associated with thinning of the articular cartilage evaluated with ultrasonography in a community-based cohort: The Nagahama study.

This study aimed to elucidate the association between joint line tenderness (JLT) of the knee and knee joint structural changes evaluated with ultrasonography (US) for the early diagnosis of knee osteoarthritis (KOA). This cross-sectional study included 121 participants (age 71.7 ± 5.8 years, 75 women) from a community-based population. Bilateral structural changes in the knee joint were evaluated with US, and the presence or absence of JLT was evaluated using a pressure algometer. Logistic regression analysis was performed to evaluate the odds ratios (ORs) of US findings for the presence of JLT. Moreover, when the analysis was limited to knees with pre-/early radiographic KOA, the ORs were also calculated using logistic regression analysis. Among the 242 knees, 38 had medial JLT, which was significantly associated with female sex (OR 11.87) and loss of cartilage thickness of the distal medial femoral condyle (CTh-MFC) (OR 0.12). Among 96 knees with Kellgren-Lawrence grade ≤ 2, 18 knees had medial JLT, which was also significantly associated with loss of CTh-MFC (OR 0.07) and medial osteophytes (OR 2.01). JLT is significantly associated with thinning of the femoral cartilage and larger osteophytes in elderly patients, even in those with pre-/early radiographic KOA.

Serum level of Adrenomedullin in patients with primary knee osteoarthritis; relation to disease severity

BackgroundAdrenomedullin (AM) is a peptide which was suggested to be involved in the pathogenesis of osteoarthritis through its anti-inflammatory and anti-apoptotic effect. AM was found to be elevated in some inflammatory rheumatic diseases as rheumatoid arthritis and ankylosing spondylitis.The current study was performed to measure serum Adrenomodullin (AM) concentrations in patients with primary knee osteoarthritis (KOA) and to assess association with severity of the disease. The study was performed on 50 patients with primary KOA diagnosed according to American College of Rheumatology (ACR) Revised Criteria for Early Diagnosis of Knee Osteoarthritis and 20 age- and sex-matched controls with no clinical features of KOA. The Kellgren and Lawrence (KL) classification was used to evaluate the disease severity of knee OA. Disease activity was assessed by The Western Ontario and McMaster Universities Arthritis Index (WOMAC). Blood samples had been collected from patients with OA and controls for assessing Adrenomodullin in patients’ sera by ELISA.ResultsThere were a significant increase in serum Adrenomedullin concentrations in KOA patients compared to controls (10.64 ±19.2 ng/ml vs. 1.39 ±1.6 ng/ml in cases and controls respectively) (p value = 0.036). There was positive significant correlation of serum Adrenomedullin levels with KL grades (r=0.608, p value <0.001). OA patients with VAS score >6 have significantly higher serum Adrenomedullin levels than OA patients with VAS Score <6. No detected significant correlation between any of (patients’ age, BMI, disease duration, tenderness score, and WOMAC score) with serum Adrenomedullin levels among studied OA cases (p values >0.05).ConclusionThis study concluded that serum Adrenomedullin (AM) level is elevated in patients with KOA and is positively correlated with the severity of disease.

Correlation Analysis of C-terminal telopeptide of collagen type II and Interleukin-1β for Early Diagnosis of Knee Osteoarthritis.

ObjectiveTo analyze the correlation between the Kellgren–Lawrence (K‐L) score of knee osteoarthritis (KOA) patients with different degrees and their urine concentration of C‐terminal telopeptide of collagen type II (CTX‐II) and interleukin‐1β (IL‐1β), and to further evaluate the diagnostic value of CTX‐II and IL‐1β during the pathological process by producing an experimental osteoarthritis (OA) model in rabbits.MethodsFrom 1 January 2017 to 31 December 2018, a total of 34 subjects (7 mild, 9 moderate, 9 severe arthritis patients, and 9 healthy individuals) comprising 16 men and 18 women were included in this study. Patients were diagnosed according to the American College of Rheumatology (ACR) criteria. The urine of all subjects was collected to detect the concentration of CTX‐II and IL‐1β. The rabbits in the KOA group were subjected to protease (control group with saline) injection into the articular cavity of their right knees and immobilization with gypsum. We used radiological and histological examination to identify the KOA model. ELISA was applied to investigate the concentrations of CTX‐II and IL‐1β in urine and serum, and Spearman's rank correlation analysis was used to analyze the correlation.ResultsThere was no significant difference in the mean ages and body mass index (BMI) between groups. The mean ages of mild, moderate, and severe arthritis patients and healthy individuals were 54.29 ± 5.76, 58.44 ± 6.44, 59.89 ± 6.75, and 56.67 ± 4.18 years, respectively. The mean BMI of mild, moderate, and severe arthritis patients and healthy individuals were 23.59 ± 1.56, 23.57 ± 2.06, 24.46 ± 1.64, and 23.42 ± 1.35 kg/m2, respectively. The Kellgren–Lawrence (K‐L) score was higher with the aggravation of KOA. The K‐L scores of mild, moderate, and severe KOA patients were 1.14 ± 0.38, 2.56 ± 0.53, and 3.63 ± 0.52, respectively. The KOA symptoms of patients became more severe, with not only increased K‐L scores but also elevated concentrations of CTX‐II and IL‐1β. Moreover, there was a positive correlation between CTX‐II and IL‐1β of all subjects (r = 0.974, P < 0.001), between K‐L score and urine concentration of CTX‐II (r = 0.900, P < 0.001), and between K‐L score and IL‐1β (r = 0.813, P < 0.001) of all subjects. Both were significantly increased in KOA group rabbits at all time points after surgery. The serum concentration of CTX‐II and IL‐1β was elevated as early as in the 2nd week (3.69 and 4.25 times) and reached a peak (5.41 and 7.23 times) in the 4th week after surgery. Then, until 12 weeks after surgery, the CTX‐II and IL‐1β concentrations in the KOA group were slightly reduced and remained around 4.5 and 6.3 times that in the control group. Moreover, there was a positive correlation between the serum concentration of IL‐1β and CTX‐II (r = 0.967, P < 0.001).ConclusionCTX‐II and IL‐1β, which were significantly increased during the process of KOA, can be used as biomolecular markers to provide guidelines for early diagnosis and treatment of KOA.

Knee-Cartilage Segmentation and Thickness Measurement from 2D Ultrasound.

Ultrasound (US) could become a standard of care imaging modality for the quantitative assessment of femoral cartilage thickness for the early diagnosis of knee osteoarthritis. However, low contrast, high levels of speckle noise, and various imaging artefacts hinder the analysis of collected data. Accurate, robust, and fully automatic US image-enhancement and cartilage-segmentation methods are needed in order to improve the widespread deployment of this imaging modality for knee-osteoarthritis diagnosis and monitoring. In this work, we propose a method based on local-phase-based image processing for automatic knee-cartilage image enhancement, segmentation, and thickness measurement. A local-phase feature-guided dynamic-programming approach is used for the fully automatic localization of knee-bone surfaces. The localized bone surfaces are used as seed points for automating the seed-guided segmentation of the cartilage. We evaluated the Random Walker (RW), watershed, and graph-cut-based segmentation methods from 200 scans obtained from ten healthy volunteers. Validation against manual expert segmentation achieved a mean dice similarity coefficient of 0.90, 0.86, and 0.84 for the RW, watershed, and graph-cut segmentation methods, respectively. Automatically segmented cartilage regions achieved 0.18 mm localization accuracy compared to manual expert thickness measurement.

Weighted cumulative exposure models helped identify an association between early knee-pain consultations and future knee OA diagnosis

ObjectiveTo establish the association between prior knee-pain consultations and early diagnosis of knee osteoarthritis (OA) by weighted cumulative exposure (WCE) models. Study Design and SettingData were from an electronic health care record (EHR) database (Consultations in Primary Care Archive). WCE functions for modeling the cumulative effect of time-varying knee-pain consultations weighted by recency were derived as a predictive tool in a population-based case-control sample and validated in a prospective cohort sample. Two WCE functions ([i] weighting of the importance of past consultations determined a priori; [ii] flexible spline-based estimation) were comprehensively compared with two simpler models ([iii] time since most recent consultation; total number of past consultations) on model goodness of fit, discrimination, and calibration both in derivation and validation phases. ResultsPeople with the most recent and most frequent knee-pain consultations were more likely to have high WCE scores that were associated with increased risk of knee OA diagnosis both in derivation and validation phases. Better model goodness of fit, discrimination, and calibration were observed for flexible spline-based WCE models. ConclusionWCE functions can be used to model prediagnostic symptoms within routine EHR data and provide novel low-cost predictive tools contributing to early diagnosis.

Ultrasonographic evaluation of the femoral cartilage thickness in patients with hypothyroidism.

[Purpose] The aim of this study was to investigate the effects of hypothyroidism on femoral cartilage thickness by using ultrasound, which has been found to be useful in the early diagnosis of knee osteoarthritis. [Subjects and Methods] Forty patients diagnosed with hypothyroidism and 30 age-, gender-, smoking status, physical activity-, and body mass index-matched healthy subjects were enrolled. The thickness of the femoral articular cartilage was measured using a 7- to 12-MHz linear probe. Three mid-point measurements were taken from each knee at the lateral condyle, intercondylar area, and medial condyle. [Results] Age, gender, body mass index, smoking status, and physical activity were similar between the groups, but patients with hypothyroidism had thinner femoral cartilage than the healthy controls at all measurement sites. Nonetheless, the differences were not statistically significant (except in the case of the left medial condyle). [Conclusion] Ultrasonographic measurement of femoral cartilage thickness may be useful in the early diagnosis of knee osteoarthritis in patients with hypothyroidism.

Serum metabolites as potential biomarkers for diagnosis of knee osteoarthritis.

Knee osteoarthritis (OA) is a highly prevalent chronic degenerative joint disease that mainly affects the elderly population. The aim of this study was to investigate serum signature metabolites as potential biomarkers for early diagnosis of knee OA. Global serum metabolic profiles of 40 patients with knee OA and 20 healthy controls (HC) were analyzed by ultra-performance liquid chromatography coupled to mass spectrometry. An OA-specific metabolic profile was established that can clearly discriminate patients with OA from HCs. Fourteen metabolites that are involved in the metabolism of amino acids, purine, energy, glycolysis, fatty acids, and lipids were significantly altered in patients with OA compared to HCs. These metabolites could be potentially used as biomarkers for the diagnosis of knee OA.

Normal plantar weight distribution pattern and its variations with change of functional position and its comparison with patients of knee osteoarthritis

Early osteoarthritic changes at the knee result in altered plantar weight distribution pattern during stand, minisquat, squat and one leg stand positions. To study and quantify these plantar weight distribution variations with changes in static functional position, a cross-sectional study was conducted. A total of 202 subjects, 92 healthy people (control group) and 110 with early knee osteoarthritis, participated in the study. The plantar weight distribution and its variations with change in functional position were assessed using footplate, while the functional disability status was assessed using WOMAC (Western Ontario and McMaster Universities Arthritis Index) & CIFKAS (Composite Indian Functional Knee Assessment Scale). The participants were allocated into two groups i.e. group-1 and group-2. The participants in group-1 had no knee complaint and those in group-2 had diagnosis of early knee osteoarthritis. Independent test was used for the statistical analysis. Significant difference between the groups was observed for the percentage plantar weight (load) distribution during stand (p value <.001 to .005), minisquat (p value <.001 to .022), left leg stand (p value <.001 to .003) and right leg stand (p value <.001 to .008) and Pain f Footplate; Functional position; Functional Disability; Plantar weight distribution