Search Results (25)

Certain molecules found on the surface or within the cargo of extracellular vesicles (EVs) are linked to osteoarthritis (OA) severity and progression. We aimed to identify plasma pathogenic EV subpopulations that can predict knee radiographic OA (rOA) progression. We analyzed the mass spectrometry-based proteomic data of plasma EVs and synovial fluid (SF) EVs from knee OA patients (n = 16, 50% female). The identified surface markers of interest were further evaluated in plasma EVs from an independent cohort of knee OA patients (n = 30, 47% female) using flow cytometry. A total of 199 peptides with significant correlation between plasma and SF EVs were identified. Of these, 41.7% were linked to immune system processes, 15.5% to inflammatory responses, and 16.7% to the complement system. Crucially, five previously identified knee rOA severity-indicating surface markers—FGA, FGB, FGG, TLN1, and AMBP—were confirmed on plasma EV subpopulations in an independent cohort. These markers’ baseline frequencies on large plasma EVs predicted rOA progression with an AUC of 0.655–0.711. Notably, TLN1 was expressed in OA joint tissue, whereas FGA, FGB, FGG, and AMBP were predominantly liver derived. These surface markers define specific pathogenic EV subpopulations, offering potential OA prognostic biomarkers and novel therapeutic targets for disease modification. Full article

Objective: Despite long simulation times, recently developed finite element analysis (FEA) models of knee joints have demonstrated their suitability for predicting individual risk of onset and progression of knee osteoarthritis. Therefore, the objective of this study was to assess the feasibility of machine learning (ML) to replicate outcomes obtained from FEA when simulating mechanical responses and predicting cartilage degeneration within the knee joint. Design: Two ML models based on the Gaussian Process Regression (GPR) algorithms were developed. The first model (GPR1) utilized age, weight, and anatomical joint dimensions as predictor variables to predict tissue mechanical responses and cartilage degeneration based on FEA data. The second model (GPR2) utilized age, weight, height, and gender to predict anatomical joint dimensions, which were then used as inputs in the GPR1 model. Finally, the GPR1 and combined GPR1+GPR2 models were used to investigate the importance of clinical imaging when making personalized predictions for knees from healthy subjects with no history of knee injuries. Results: In the GPR1 model, R² of 0.9 was exceeded for most of the predicted mechanical parameters. The GPR2 model was able to predict knee shape with R² of 0.67–0.9. Both GPR1 and combined GPR1+GPR2 models offered equally good performances (AUC = 0.73–0.74) in classifying patients at high risk for the onset and development of knee osteoarthritis. Conclusions: In the future, real-time and easy-to-use GPR models may provide a rapid technology to evaluate mechanical responses within the knee for researchers or clinicians who have no former knowledge of FEA. Full article

Objective: Osteoarthritis is linked to dementia, but no longitudinal studies have established this connection. This prospective cohort study from the Locomotive Syndrome and Health Outcome in Aizu Cohort Study (LOHAS) aimed to determine if knee osteoarthritis (KOA) independently predicts dementia in adults aged 65 and above. Methods: Participants were classified by the Kellgren–Laurence scale into no/minimal KOA (grades 0 and I) and definitive KOA (grade II or higher). We analyzed dementia incidence from 2009 to 2015 using long-term care insurance data, adjusting for age, sex, vascular risks, depressive symptoms, and activity levels. Results: Out of 1089 participants (58.9% female, average age 72.5), 72.0% had definitive KOA. Dementia occurrence was significantly higher in the definitive group (8.4%) compared to the no/minimal group (3.0%) (p < 0.001). A log-rank test and Cox regression analysis confirmed these findings, showing an adjusted hazard ratio of 2.29 (confidence interval: 1.12–4.68) for dementia in those with definitive KOA. Conclusions: These results suggest that KOA is a significant risk factor for dementia, highlighting the importance of addressing contributing factors in KOA patients to potentially slow the progression of dementia. Full article

Knee osteoarthritis (OA) affects over 650 million patients worldwide. Total knee replacement is aimed at end-stage OA to relieve symptoms of pain, stiffness and reduced mobility. However, the role of imaging modalities in monitoring symptomatic disease progression remains unclear. This study aimed to compare machine learning (ML) models, with and without imaging features, in predicting the two-year Western Ontario and McMaster Universities Arthritis Index (WOMAC) score for knee OA patients. We included 2408 patients from the Osteoarthritis Initiative (OAI) database, with 629 patients from the Multicenter Osteoarthritis Study (MOST) database. The clinical dataset included 18 clinical features, while the imaging dataset contained an additional 10 imaging features. Minimal Clinically Important Difference (MCID) was set to 24, reflecting meaningful physical impairment. Clinical and imaging dataset models produced similar area under curve (AUC) scores, highlighting low differences in performance AUC < 0.025). For both clinical and imaging datasets, Gradient Boosting Machine (GBM) models performed the best in the external validation, with a clinically acceptable AUC of 0.734 (95% CI 0.687–0.781) and 0.747 (95% CI 0.701–0.792), respectively. The five features identified included educational background, family history of osteoarthritis, co-morbidities, use of osteoporosis medications and previous knee procedures. This is the first study to demonstrate that ML models achieve comparable performance with and without imaging features. Full article

Knee osteoarthritis is a chronic, progressive disease that rapidly progresses to severe stages. Reliable and accurate diagnosis, combined with the implementation of preventive lifestyle modifications before irreversible damage occurs, can effectively protect patients from becoming an inactive population. Artificial intelligence continues to play a pivotal role in computer-aided diagnosis with increasingly convincing accuracy, particularly in identifying the severity of knee osteoarthritis according to the Kellgren–Lawrence (KL) grading scale. The primary objective of this literature review is twofold. Firstly, it aims to provide a systematic analysis of the current literature on the main artificial intelligence models used recently to predict the severity of knee osteoarthritis from radiographic images. Secondly, it constitutes a critical review of the different methodologies employed and the key elements that have improved diagnostic performance. Ultimately, this study demonstrates that the considerable success of artificial intelligence systems will reinforce healthcare professionals’ confidence in the reliability of machine learning algorithms, facilitating more effective and faster treatment for patients afflicted with knee osteoarthritis. In order to achieve these objectives, a qualitative and quantitative analysis was conducted on 60 original research articles published between 1 January 2018 and 15 May 2024. Full article

Background: Delay in diagnosis and therapy in patients with arthritis commonly leads to progressive articular damage. The study aimed to investigate the immunohistochemical reactivity of synovial cytokines associated with inflammation and the bone erosives/neoformatives processes among individuals diagnosed with psoriatic arthritis (PsA), rheumatoid arthritis (RA), osteoarthritis (OA), and radiographic axial spondyloarthritis (r-axSpA), with the intention of identifying potential biomarkers. Methods: Specimens were collected from the inflamed knee joints of patients referred for arthroscopic procedures, and the synovial tissue (ST) was prepared for quantifying protein expression through immunohistochemical analysis (% expressed in Ratio_Area-Intensity) for TGF-β1, IL-17A, Dkk1, BMP2, BMP4, and Wnt5b. The collected data underwent thorough analysis and examination of their predictive capabilities utilising receiver operating characteristic (ROC) curves. Results: Valid synovial tissue samples were acquired from 40 patients for IHC quantification analysis. Initially, these patients had not undergone treatment with biologics. However, after 5 years, 4 out of 13 patients diagnosed with PsA and two out of nine patients diagnosed with RA had commenced biologic treatments. Individuals with early PsA who received subsequent biologic treatment exhibited significantly elevated IHC reactivity in ST for TGF-β1 (p = 0.015). Additionally, patients with both PsA and RA who underwent biologic therapy displayed increased IHC reactivity for IL-17A (p = 0.016), TGF-β1 (p = 0.009), and Dkk1 (p = 0.042). ROC curve analysis of IHC reactivity for TGF-β1, Dkk1, and IL-17A in the synovial seems to predict future treatment with biologics in the next 5 years with the area under the curve (AUC) of a combined sum of the three values: AUC: 0.828 (95% CI: 0.689–0.968; p 0.005) S 75% E 84.4%. Conclusions: Higher synovial immunohistochemistry reactivity of IL-17A, Dkk1, and TGF-β1 in patients with early psoriatic arthritis and rheumatoid arthritis may serve as potential indicators for predicting the necessity of utilising biologic treatments. Full article

Imaging biomarkers permit improved approaches to identify the most at-risk patients encountering knee osteoarthritis (KOA) progression. This study aimed to investigate the utility of trabecular bone texture (TBT) extracted from plain radiographs, associated with a set of clinical, biochemical, and radiographic data, as a predictor of long-term radiographic KOA progression. We used data from the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium dataset. The reference model made use of baseline TBT parameters adjusted for clinical covariates and radiological scores. Several models based on a combination of baseline and 24-month TBT variations (TBT∆TBT) were developed using logistic regression and compared to those based on baseline-only TBT parameters. All models were adjusted for baseline clinical covariates, radiological scores, and biochemical descriptors. The best overall performances for the prediction of radio-symptomatic, radiographic, and symptomatic progression were achieved using TBT∆TBT parameters solely, with area under the ROC curve values of 0.658 (95% CI: 0.612–0.705), 0.752 (95% CI: 0.700–0.804), and 0.698 (95% CI: 0.641–0.756), respectively. Adding biochemical markers did not significantly improve the performance of the TBT∆TBT-based model. Additionally, when TBT values were taken from the entire subchondral bone rather than just the medial, lateral, or central compartments, better results were obtained. Full article

Monitoring human movement is highly relevant in mobile health applications. Textile-based wearable solutions have the potential for continuous and unobtrusive monitoring. The precise estimation of joint angles is important in applications such as the prevention of osteoarthritis or in the assessment of the progress of physical rehabilitation. We propose a textile-based wearable device for knee angle estimation through capacitive sensors placed in different locations above the knee and in contact with the skin. We exploited this modality to enhance the baseline value of the capacitive sensors, hence facilitating readout. Moreover, the sensors are fabricated with only one layer of conductive fabric, which facilitates the design and realization of the wearable device. We observed the capability of our system to predict knee sagittal angle in comparison to gold-standard optical motion capture during knee flexion from a seated position and squats: the results showed an R² coefficient between 0.77 and 0.99, root mean squared errors between 4.15 and 12.19 degrees, and mean absolute errors between 3.28 and 10.34 degrees. Squat movements generally yielded more accurate predictions than knee flexion from a seated position. The combination of the data from multiple sensors resulted in R² coefficient values of 0.88 or higher. This preliminary work demonstrates the feasibility of the presented system. Future work should include more participants to further assess the accuracy and repeatability in the presence of larger interpersonal variability. Full article

Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by progressive cartilage degradation and joint inflammation. As the most common aging-related joint disease, OA is marked by inadequate extracellular matrix synthesis and the breakdown of articular cartilage. However, traditional diagnostic methods for OA, relying on clinical assessments and radiographic imaging, often need to catch up in detecting early-stage disease or i accurately predicting its progression. Consequently, there is a growing interest in identifying reliable biomarkers that can facilitate early diagnosis and prognosis of OA. MicroRNAs (miRNAs) have emerged as potential candidates due to their involvement in various cellular processes, including cartilage homeostasis and inflammation. This review explores the feasibility of circulating miRNAs as diagnostic and prognostic biomarkers in OA, focusing on knee OA while shedding light on the challenges and opportunities associated with their implementation in clinical practice. Full article

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%. Full article

Osteoarthritis (OA) is a chronic disease common in the elderly population and imposes significant health and economic burden. Total joint replacement is the only currently available treatment but does not prevent cartilage degeneration. The molecular mechanism of OA, especially the role of inflammation in disease progression, is incompletely understood. We collected knee joint synovial tissue samples of eight OA patients and two patients with popliteal cysts (controls), measured the expression levels of lncRNAs, miRNAs, and mRNAs in these tissues by RNA-seq, and identified differentially expressed genes (DEGs) and key pathways. In the OA group, 343 mRNAs, 270 lncRNAs, and 247 miRNAs were significantly upregulated, and 232 mRNAs, 109 lncRNAs, and 157 miRNAs were significantly downregulated. mRNAs potentially targeted by lncRNAs were predicted. Nineteen overlapped miRNAs were screened based on our sample data and GSE 143514 data. Pathway enrichment and functional annotation analyses showed that the inflammation-related transcripts CHST11, ALDH1A2, TREM1, IL-1β, IL-8, CCL5, LIF, miR-146a-5p, miR-335-5p, lncRNA GAS5, LINC02288, and LOC101928134 were differentially expressed. In this study, inflammation-related DEGs and non-coding RNAs were identified in synovial samples, suggesting that competing endogenous RNAs have a role in OA. TREM1, LIF, miR146-5a, and GAS5 were identified to be OA-related genes and potential regulatory pathways. This research helps elucidate the pathogenesis of OA and identify novel therapeutic targets for this disorder. Full article

Factors affecting the progression rate and fate of osteoarthritis need to be analyzed when considering patient-specific situation. This study aimed to identify the rate of remarkable progression and fate of primary knee osteoarthritis based on patient-specific situations. Between May 2003 and May 2019, 83,280 patients with knee pain were recruited for this study from the clinical data warehouse. Finally, 2492 knees with pain that were followed up for more than one year were analyzed. For analyzing affecting factors, patient-specific information was categorized and classified as demographic, radiologic, social, comorbidity disorders, and surgical intervention data. The degree of contribution of factors to the progression rate and the fate of osteoarthritis was analyzed. Bone mineral density (BMD), Kellgren–Lawrence (K–L) grade, and physical occupational demands were major contributors to the progression rate of osteoarthritis. Hypertension, initial K–L grade, and physical occupational demands were major contributors to the outcome of osteoarthritis. The progression rate and fate of osteoarthritis were mostly affected by the initial K–L grade and physical occupational demands. Patients who underwent surgical intervention for less than five years had the highest proportion of initial K–L grade 2 (49.0%) and occupations with high physical demand (41.3%). In identifying several contributing factors, the initial K–L grade and physical occupational demands were the most important factors. BMD and hypertension were also major contributors to the progression and fate of osteoarthritis, and the degree of contribution was lower compared to the two major factors. Full article

Conventional radiography remains the most widely available imaging modality in clinical practice in knee osteoarthritis. Recent research has been carried out to develop novel radiographic biomarkers to establish the diagnosis and to monitor the progression of the disease. The growing number of publications on this topic over time highlights the necessity of a renewed review. Herein, we propose a narrative review of a selection of original full-text articles describing human studies on radiographic imaging biomarkers used for the prediction of knee osteoarthritis-related outcomes. To achieve this, a PubMed database search was used. A total of 24 studies were obtained and then classified based on three outcomes: (1) prediction of radiographic knee osteoarthritis incidence, (2) knee osteoarthritis progression and (3) knee arthroplasty risk. Results showed that numerous studies have reported the relevance of joint space narrowing score, Kellgren–Lawrence score and trabecular bone texture features as potential bioimaging markers in the prediction of the three outcomes. Performance results of reviewed prediction models were presented in terms of the area under the receiver operating characteristic curves. However, fair and valid comparisons of the models’ performance were not possible due to the lack of a unique definition of each of the three outcomes. Full article

Background and Objectives: Osteoarthritis (OA) of the knee is a degenerative disorder characterized by damage to the joint cartilage, pain, swelling, and walking disability. The purpose of this study was to assess whether demographic and radiologic parameters (knee diameters and knee cross-sectional area from magnetic resonance (MR) images) could be used as surrogate biomarkers for the prediction of OA. Materials and Methods: The knee diameters and cross-sectional areas of 481 patients were measured on knee MR images, and the corresponding demographic parameters were extracted from the patients’ clinical records. The images were graded based on the modified Outerbridge arthroscopic classification that was used as ground truth. Receiver-operating characteristic (ROC) analysis was performed on the collected data. Results: ROC analysis established that age was the most accurate predictor of severe knee cartilage degeneration (corresponding to Outerbridge grades 3 and 4) with an area under the curve (AUC) of the specificity–sensitivity plot of 0.865 ± 0.02. An age over 41 years was associated with a sensitivity and specificity for severe degeneration of 82.8% (CI: 77.5–87.3%), and 76.4% (CI: 70.4–81.6%), respectively. The second-best degeneration predictor was the normalized knee cross-sectional area, with an AUC of 0.767 ± 0.04), followed by BMI (AUC = 0.739 ± 0.02), and normalized knee maximal diameter (AUC = 0.724 ± 0.05), meaning that knee degeneration increases with increasing knee diameter. Conclusions: Age is the best predictor of knee damage progression in OA and can be used as surrogate marker for knee degeneration. Knee diameters and cross-sectional area also correlate with the extent of cartilage lesions. Though less-accurate predictors of damage progression than age, they have predictive value and are therefore easily available surrogate markers of OA that can be used also by general practitioners and orthopedic surgeons. Full article

Recently, many diseases have negatively impacted people’s lifestyles. Among these, knee osteoarthritis (OA) has been regarded as the primary cause of activity restriction and impairment, particularly in older people. Therefore, quick, accurate, and low-cost computer-based tools for the early prediction of knee OA patients are urgently needed. In this paper, as part of addressing this issue, we developed a new method to efficiently diagnose and classify knee osteoarthritis severity based on the X-ray images to classify knee OA in (i.e., binary and multiclass) in order to study the impact of different class-based, which has not yet been addressed in previous studies. This will provide physicians with a variety of deployment options in the future. Our proposed models are basically divided into two frameworks based on applying pre-trained convolutional neural networks (CNN) for feature extraction as well as fine-tuning the pre-trained CNN using the transfer learning (TL) method. In addition, a traditional machine learning (ML) classifier is used to exploit the enriched feature space to achieve better knee OA classification performance. In the first one, we developed five classes-based models using a proposed pre-trained CNN for feature extraction, principal component analysis (PCA) for dimensionality reduction, and support vector machine (SVM) for classification. While in the second framework, a few changes were made to the steps in the first framework, the concept of TL was used to fine-tune the proposed pre-trained CNN from the first framework to fit the two classes, three classes, and four classes-based models. The proposed models are evaluated on X-ray data, and their performance is compared with the existing state-of-the-art models. It is observed through conducted experimental analysis to demonstrate the efficacy of the proposed approach in improving the classification accuracy in both multiclass and binary class-based in the OA case study. Nonetheless, the empirical results revealed that the fewer multiclass labels used, the better performance achieved, with the binary class labels outperforming all, which reached a 90.8% accuracy rate. Furthermore, the proposed models demonstrated their contribution to early classification in the first stage of the disease to help reduce its progression and improve people’s quality of life. Full article