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Journal articles on the topic 'Knee osteoarthritis detection'
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1
Cueva, Joseph Humberto, Darwin Castillo, Héctor Espinós-Morató, David Durán, Patricia Díaz, and Vasudevan Lakshminarayanan. "Detection and Classification of Knee Osteoarthritis." Diagnostics 12, no. 10 (2022): 2362. http://dx.doi.org/10.3390/diagnostics12102362.
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Osteoarthritis (OA) affects nearly 240 million people worldwide. Knee OA is the most common type of arthritis, especially in older adults. Physicians measure the severity of knee OA according to the Kellgren and Lawrence (KL) scale through visual inspection of X-ray or MR images. We propose a semi-automatic CADx model based on Deep Siamese convolutional neural networks and a fine-tuned ResNet-34 to simultaneously detect OA lesions in the two knees according to the KL scale. The training was done using a public dataset, whereas the validations were performed with a private dataset. Some problems of the imbalanced dataset were solved using transfer learning. The model results average of the multi-class accuracy is 61%, presenting better performance results for classifying classes KL-0, KL-3, and KL-4 than KL-1 and KL-2. The classification results were compared and validated using the classification of experienced radiologists.
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Espinós-Morató, HECTOR. "Detection and classification of knee osteoarthritis." Diagnostics 12, no. 10 (2022): 2362. https://doi.org/10.3390/diagnostics12102362.
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Osteoarthritis (OA) affects nearly 240 million people worldwide. Knee OA is the most common type of arthritis, especially in older adults. Physicians measure the severity of knee OA according to the Kellgren and Lawrence (KL) scale through visual inspection of X-ray or MR images. We propose a semi-automatic CADx model based on Deep Siamese convolutional neural networks and a fine-tuned ResNet-34 to simultaneously detect OA lesions in the two knees according to the KL scale. The training was done using a public dataset, whereas the validations were performed with a private dataset. Some problems of the imbalanced dataset were solved using transfer learning. The model results average of the multi-class accuracy is 61%, presenting better performance results for classifying classes KL-0, KL-3, and KL-4 than KL-1 and KL-2. The classification results were compared and validated using the classification of experienced radiologists.
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Bonanzinga, Tommaso, Pietro Conte, Giuseppe Anzillotti, et al. "Native intra-articular knee microbiome is a matter of facts: a systematic review of clinical evidence." EFORT Open Reviews 9, no. 10 (2024): 969–79. http://dx.doi.org/10.1530/eor-23-0191.
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Purpose Growing interest surrounds the role of human gut microbiome in the development of degenerative pathologies such as osteoarthritis (OA), but microbes have recently been detected also in other sites previously considered to be sterile. Evidence emerged suggesting that even native and osteoarthritic knee joints may host several microbial species possibly involved in the osteoarthritic degeneration. This is the first systematic review critically collecting all the available evidence on the existence and composition of knee intra-articular microbiome. Methods A systematic research on the PubMed, Cochrane and Google Scholar databases was performed. Human clinical studies investigating the presence of intra-articular microbiome in native osteoarthritic knee joints with next-generation sequencing techniques were collected. Results A total of eight studies were included reporting data on 255 knees. All the included studies reported evidence supporting the existence of an intra-articular microbiome in native knee joints, with detection rates varying from 5.8% to 100%. Bacteria from the Proteobacteria phylum were found to be among the most identified followed by the Actinobacteria, Firmicutes, Fusobacteria, and Bacteroideta phyla. Proteobacteria phylum were also found to be more common in osteoarthritic knees when compared to healthy joints. Furthermore, several pathways correlating those microbes to knee OA progression have been suggested and summarized in this review. Conclusions Evidence collected in this systematic review suggests that the native knee joint, previously presumed to be a sterile environment, hosts a peculiar intra-articular microbiome with a unique composition. Furthermore, its alteration may have a link with the progression of knee osteoarthritis.
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Salama, Ahmed, Kamel Rahouma, and Fatma Elzahraa Mansour. "Knee osteoarthritis automatic detection using U-Net." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2122. http://dx.doi.org/10.11591/ijai.v13.i2.pp2122-2130.
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Knee osteoarthritis or OA is one of the most common diseases that can affect the elderly and overweight people. OA is occur as the result of wear and tear and progressive loss of articular cartilage. Kellgren-Lawrence system is a common method of classifying the severity of osteoarthritis depends on knee joint width. According to Kellgren-Lawrence, knee osteoarthritis is divided into five classes; one class represents a normal knee and the others represent four levels of knee osteoarthritis. In this work, we aim to automatically detect knee OA according to the Kellgren-Lawrence classification. The proposed system uses the U-Net architecture. The overall system yielded an accuracy of 96.3% during training.
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Alshamrani, Hassan A., Mamoon Rashid, Sultan S. Alshamrani, and Ali H. D. Alshehri. "Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach." Healthcare 11, no. 9 (2023): 1206. http://dx.doi.org/10.3390/healthcare11091206.
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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%.
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C, Yashas, Suman K R, Vighnesh S, and Sudeep K U. "Knee Osteoarthritis Detection and Severity Prediction Using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 3441–49. http://dx.doi.org/10.22214/ijraset.2023.51025.
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Abstract: The worst kind of arthritis is knee osteoarthritis (KOA). If left untreated, it can require a knee substitute. Right away KOA diagnosis is therefore essential for the best possible care. The manual detection of KOA is a laborious and susceptible to mistakes process. Computational methods are required for timely and accurate detection. The failure of the connective tissue in the knee joint, which results in bone fragments rubbing against other bones, leads to osteoarthritis of the knee. The wear and tear hurt, stiffens, and inflames the knees. Even though osteoarthritis of the knee cannot be cured, there are several treatments that can help to reduce symptoms and decrease the condition's development. A radiologist grades the anomalies on knee X-ray pictures according to their severity using Kellgren-Lawrence's five-point ordinal scale (0–4). The datasets must be trained first using the CNN approach, which is used in this study. Various convolutional layers emerge on the CNN algorithm during training, and the precision increases with each layer. Once uploaded, the X-ray image is shrunk, its color is turned to grey, and several Convolutional layers are applied to it with the help of the CNN algorithm.
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Ahmed, Salama Abdellatif, Hussien Rahouma Kamel, and E. Mansour Fatma. "Knee osteoarthritis automatic detection using U-Net." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2122–30. https://doi.org/10.11591/ijai.v13.i2.pp2122-2130.
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Knee osteoarthritis or OA is one of the most common diseases that can affect the elderly and overweight people. OA is occurred as the result of wear, tear, and progressive loss of articular cartilage. Kellgren-Lawrence system is a common method of classifying the severity of osteoarthritis depends on knee joint width. According to Kellgren-Lawrence, knee OA is divided into five classes; one class represents a normal knee and the others represent four levels of knee OA. In this work, we aim to automatically detect knee OA according to the Kellgren-Lawrence classification. The proposed system uses the U-Net architecture. The overall system yielded an accuracy of 96.3% during training.
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Takeshi Tokoshima, Kazunori Hase, Rui Gong, and Makoto Yoshida. "Non-Invasive Monitoring of Knee Osteoarthritis Severity Using Vibration Stimulation." Proceedings of Engineering and Technology Innovation 29 (February 10, 2025): 01–10. https://doi.org/10.46604/peti.2024.14079.
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This study aims to explore the application of vibration stimulation for the early detection and assessment of knee osteoarthritis severity, using a porcine knee joint. Accelerometers are attached to the femurs and tibias to measure vibratory responses under simulated osteoarthritic conditions. Frequency response functions are generated from the acceleration data and quantified using the root mean square deviation (RMSD) relative to baseline conditions. To ensure the reliability of the results, a coherence filter is applied, confirming significant differences across various stages of joint injury. The RMSD analysis demonstrates the technique's ability to detect phase differences, particularly within the 1000 Hz frequency range. These findings suggest that vibration stimulation could be a feasible non-invasive diagnostic method for assessing osteoarthritis severity in humans. This study highlights the potential of vibration-based diagnostics as an innovative approach for the early detection of osteoarthritis.
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Puthuru Kavya, Kouluri Sreeja Reddy, Tholla Ujwala, Amuru Mounitha, and Ms. Rekha M S. "Knee Osteoarthritis Detection and It’s Severity CNN." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 173–78. http://dx.doi.org/10.32628/cseit2410322.
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Osteoarthritis (OA) of the knee is a common degenerative joint disease that is characterized by inflammation and cartilage degradation, which results in pain and impairment. For prompt intervention and care, early detection and precise assessment of the severity of OA are essential. In this research, we offer a unique method for automated knee OA diagnosis and severity assessment using medical imaging data, especially X-ray images, using convolutional neural networks (CNNs). Our CNN architecture is intended to identify complex features from knee X-rays and categorize them into various OA severity levels, from moderate to severe. A sizable dataset of knee X-ray pictures with accompanying OA severity scores is used by the suggested model. Our method shows promise in correctly detecting knee OA after thorough testing and validation on a variety of datasets.
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Patron, Anri, Leevi Annala, Olli Lainiala, Juha Paloneva, and Sami Äyrämö. "An Automatic Method for Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis." Diagnostics 12, no. 11 (2022): 2603. http://dx.doi.org/10.3390/diagnostics12112603.
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Efficient and scalable early diagnostic methods for knee osteoarthritis are desired due to the disease’s prevalence. The current automatic methods for detecting osteoarthritis using plain radiographs struggle to identify the subjects with early-stage disease. Tibial spiking has been hypothesized as a feature of early knee osteoarthritis. Previous research has demonstrated an association between knee osteoarthritis and tibial spiking, but the connection to the early-stage disease has not been investigated. We study tibial spiking as a feature of early knee osteoarthritis. Additionally, we develop a deep learning based model for detecting tibial spiking from plain radiographs. We collected and graded 913 knee radiographs for tibial spiking. We conducted two experiments: experiments A and B. In experiment A, we compared the subjects with and without tibial spiking using Mann-Whitney U-test. Experiment B consisted of developing and validating an interpretative deep learning based method for predicting tibial spiking. The subjects with tibial spiking had more severe Kellgren-Lawrence grade, medial joint space narrowing, and osteophyte score in the lateral tibial compartment. The developed method achieved an accuracy of 0.869. We find tibial spiking a promising feature in knee osteoarthritis diagnosis. Furthermore, the detection can be automatized.
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Huang, Bing, Yun Huang, Xin Ma, and Yuequn Chen. "Intelligent Algorithm-Based Magnetic Resonance for Evaluating the Effect of Platelet-Rich Plasma in the Treatment of Intractable Pain of Knee Arthritis." Contrast Media & Molecular Imaging 2022 (May 26, 2022): 1–11. http://dx.doi.org/10.1155/2022/9223928.
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The application of intelligent algorithms in the treatment of intractable pain of patients with platelet-rich plasma (PRP) knee osteoarthritis by magnetic resonance was investigated. The automatic diagnosis of magnetic resonance knee osteoarthritis was established with multiple intelligent algorithms, including gray projection algorithm, adaptive binarization algorithm, and active shape model (ASM). The difference between automatic magnetic resonance detection indexes of the patients with knee osteoarthritis and artificial measurement results was analyzed. The included patients received PRP treatment. Knee osteoarthritis MRI osteoarthritis knee scores (KOA MOAKS) and Western Ontario and McMaster Universities arthritis index (WOMAC) before and after treatment were compared. The results showed that the results of knee osteoarthritis scores, inferior angle of femur, superior angle of tibia, and tibiofemoral angle (TFA) by automatic magnetic resonance diagnostic model were entirely consistent with artificial detection results. After the treatment, the total scores of knee lateral area, interior area, central area, and patellar area were all remarkably lower than those before the treatment ( P < 0.05 ). After the treatment, knee KOA MOAKS scores and WOMAC scores were both lower than those before the treatment ( P < 0.05 ). Visual analogue scale (VAS) scores 1 week, 2 weeks, and 3 weeks after the treatment were decreased compared with those before the treatment ( P < 0.05 ). Relevant studies indicated that intelligent algorithm-based automatic magnetic resonance diagnostic knee osteoarthritis model showed good utilization values, which could provide the reference and basis for the treatment of the patients with knee osteoarthritis.
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Patil, Sahil Dinesh. "Scoliosis and Knee Osteoarthritis Classification and Detection using X-Rays." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31183.
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Medical imaging plays a pivotal role in the early detection and accurate classification of musculoskeletal disorders. This study focuses on the development of a robust and efficient system for the detection and classification of two prevalent musculoskeletal conditions: scoliosis and knee osteoarthritis, using X-ray images. Scoliosis is a lateral curvature of the spine, while knee osteoarthritis involves the degeneration of knee joint cartilage and underlying bone.The proposed system leverages state-of-the-art deep learning techniques to automatically detect and classify these conditions from X-ray images. The workflow involves three main stages: preprocessing, feature extraction, and classification. During preprocessing, the X-ray images are normalized, noise-reduced, and anatomical landmarks are identified for accurate alignment. Key Words: Detection and classification, knee osteoarthritis, ordinal classification, X-rays.
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S, Ananya, Anirudh A, Annanya ., Asha Holla, and Rosline Mary. "Knee Osteoarthritis Radiology Assistant." International Research Journal of Computer Science 10, no. 04 (2023): 69–77. http://dx.doi.org/10.26562/irjcs.2023.v1004.07.
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Knee osteoarthritis is a degenerative joint disease affecting millions worldwide. Early detection and monitoring of knee osteoarthritis are critical for effective management of the disease. Magnetic resonance imaging (MRI) is a powerful tool for detecting knee osteoarthritis, but the interpretation of MRI images can be prolonged and subjective. In this paper, we propose a deep learning-based approach for the automatic diagnosis of knee osteoarthritis from MRI images. Our approach involves a deep learning architecture known as Dense Net, which has shown promising results in image classification tasks. We also incorporate a Squeeze-and- Excitation (SE) layer into the network, which can selectively emphasize informative features in the input images. We train and validate our approach using the OAI dataset of MRI images from patients with knee osteoarthritis and healthy controls. The images will then be fed into the Dense Net with SE layers to automatically classify them as either healthy or osteoarthritis. The python script generates a sample report for the MRI scans uploaded in the portal. The patient should be able to access their report but should also be able to generate a report if they have their MRI scans. The proposed model with Dense Net architecture gives an accuracy of 88.5%. The performance of our approach will be evaluated using various metrics such as accuracy, precision, recall, F1 score, confusion matrix, and area under the receiver operating characteristic curve (AUC-ROC).
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Annu James. "Detection of Knee Osteoarthritis grade using Convolutional Neural Networks." Journal of Information Systems Engineering and Management 10, no. 5s (2025): 305–12. https://doi.org/10.52783/jisem.v10i5s.627.
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Knee OsteoArthritis (KOA) is a disease that affects a person’s quality of life. Early detection and monitoring of KOA progression is essential for effective therapy and quick recovery. A survey of the recent literatures indicates that deep learning methods can effectively assess KOA severity with improved accuracy and efficiency. Convolutional Neural Networks (CNN) help us to classify the levels of severity of Knee Osteoarthritis. The present study proposes a deep learning method in classification of osteoarthritis using Convolution Neural Networks. The study focuses on predicting the grades of input images with KL grades of Knee osteoarthritis. The study employs convolutional neural networks with the Rectified Linear Unit function (ReLU), activation function and Adam optimization algorithm to achieve high performance. The study evaluates 10 performance measures and the results indicate an improvement in performance measures when compared with existing techniques.
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Kadu, Rahul, and Sunil Pawar. "Advanced Bi-CNN for Detection of Knee Osteoarthritis using Joint Space Narrowing Analysis." Journal of Electronics, Electromedical Engineering, and Medical Informatics 7, no. 1 (2024): 80–90. https://doi.org/10.35882/jeeemi.v7i1.574.
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The prevalence of knee osteoarthritis is significantly increasing due to the expanding global ageing population and the rising incidence of obesity. Many researchers use artificial intelligence analytics for knee osteoarthritis (KOA) prediction and treatment. The majority of research is restricted to particular patient groups or attributes, such MRI, X-ray, or questionnaire groups. In our research we propose the use of advanced ortho bilinear convolutional neural network (CNN) classifier to enhance the precision of knee osteoarthritis detection through joint space narrowing analysis. Recognizing the critical need for accurate and early diagnosis in osteoarthritis, this study introduces a sophisticated approach leveraging the unique capabilities of bilinear CNNs (BiCNN). By integrating bilinear interactions within the CNN architecture, the model aims to capture convoluted spatial and channel-wise dependencies in knee radiographic images, thereby improving the capability to understated changes in osteoarthritis progression, particularly within the joint space. The proposed bilinear CNN classifier technique promises to refine the precision of knee osteoarthritis detection, providing clinicians with a powerful tool for identifying joint space narrowing with improved accuracy. Based on the experiment over unseen images, the recall was 93.04%, precision 96.33%, F1 Score was 95.46% and overall accuracy was 94.28%. Results show the superiority of the proposed method compared to other state-of-the-art methods. Hence the proposed method can be used for KOA diagnosis and KL grading in real time scenarios.
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D.Kiruthika and Judith J. "Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Modified Fully connected Convolutional Neural Networks." International Journal of Innovative Science and Research Technology 7, no. 12 (2022): 569–77. https://doi.org/10.5281/zenodo.7490772.
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Knee Osteoarthritis (OA) is an extremely common and degenerative musculoskeletal disease worldwide which creates a significant burden on patients with reduced quality of life and also on society because of its financial impact. Therefore, technical try and efforts to reduce the burden of the disease could help both patients and society. In this paper, an automated novel method is proposed with a supported combination of joint shape and modified Fully connected neural network (FCNN) based bone texture features, to differentiate between the knee radiographs with and without osteoarthritis. Moreover, an endeavor is formed to explain the bone texture using CNN. Knee radiographs from Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis (MOST) datasets are utilized in this paper. The proposed models were trained on 8000 knee radiographs from OAI and evaluated on 3500 knee radiographs from MOST. The results demonstrate that fusing the proposed shape and texture parameters achieves the state-of-the art performance in radiographic OA detection yielding area under the ROC curve (AUC) of 98.75% accuracy.
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M, Ganesh Kumar, and Agam Das Goswami. "Automatic Classification of the Severity of Knee Osteoarthritis Using Enhanced Image Sharpening and CNN." Applied Sciences 13, no. 3 (2023): 1658. http://dx.doi.org/10.3390/app13031658.
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Knee osteoarthritis is a significant cause of physical inactivity and disability. Early detection and treatment of osteoarthritis (OA) degeneration can decrease its course. Physicians’ scores may differ significantly amongst interpreters and are greatly influenced by personal experience based solely on visual assessment. Deep convolutional neural networks (CNN) in conjunction with the Kellgren–Lawrence (KL) grading system are used to assess the severity of OA in the knee. Recent research applied for knee osteoarthritis using machine learning and deep learning results are not encouraging. One of the major reasons for this was that the images taken are not pre-processed in the correct way. Hence, feature extraction using deep learning was not great, thus impacting the overall performance of the model. Image sharpening, a type of image filtering, was required to improve image clarity due to noise in knee X-ray images. The assessment used baseline X-ray images from the Osteoarthritis Initiative (OAI). On enhanced images acquired utilizing the image sharpening process, we achieved a mean accuracy of 91.03%, an improvement of 19.03% over the earlier accuracy of 72% by using the original knee X-ray images for the detection of OA with five gradings. The image sharpening method is used to advance knee joint recognition and knee KL grading.
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CENGİZLER, Çağlar, and Ayşe Gül KABAKCI. "A K-nearest neighbors-based classification approach for automated detection of knee osteoarthritis." Cukurova Medical Journal 48, no. 2 (2023): 715–22. http://dx.doi.org/10.17826/cumj.1281955.
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Purpose: Osteoarthritis is a serious condition that can significantly reduce a person’s quality of life, causing pain and stiffness in the knees and limiting their mobility. The condition progressively worsens over time, emphasizing the importance of early diagnosis. This study implemented a computer-aided classification approach to reduce the time and effort required for diagnosing knee osteoarthritis while minimizing human errors.
 Materials and Methods: Data analyzed in this study was obtained from the Osteoarthritis Initiative. A total of 165 samples were used in the study. All abnormal samples were graded as severe osteoarthritis. While 78 samples were used to test the implemented algorithm, the training process of the algorithm was completed with 87 samples. The proposed approach involves three main stages: segmenting the cartilage region through a series of image-processing operations, extracting morphological features from the defined region, and classifying samples based on these features. In the classification stage, morphological features characterizing the cartilage region were classified in the observation space, and the k-nearest neighbors algorithm was applied for automated discrimination. Accordingly, the computer utilizes the previously classified sample features to estimate the presence of pathology.
 Results: Test classifications were completed with 78 samples; 28 were previously diagnosed with osteoarthritis. Morphological measures of the training samples were accepted as a reference for abnormality. The applied classification scheme can distinguish severed cartilage regions with a 0.95% accuracy.
 Conclusion: This study demonstrates the potential effectiveness of a computer-aided approach in diagnosing knee osteoarthritis with high accuracy. The developed approach offers a promising solution for early and efficient diagnosis, enabling more timely and effective treatment strategies for osteoarthritis patients. The progressive nature of the disease makes these advancements in diagnostic methods invaluable. Future studies may focus on expanding the sample size and further refining the model for enhanced precision and broad applicability in clinical settings.
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Kumar M, Ganesh, and Agam Das Goswami. "Deep Convolutional Neural Network Classifier for Effective Knee Osteoarthritis Classification." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3 (2023): 242–49. http://dx.doi.org/10.17762/ijritcc.v11i3.6343.
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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%.
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Sabah Afroze, A., R. Tamil Selvi, M. Parisa Beham, J. Judith, and S. Sathiya Pandiya Lakshmi. "A Machine Learning based Approach for Detection of Osteoarthritis using Thermal Images." Journal of Innovative Image Processing 5, no. 2 (2023): 115–26. http://dx.doi.org/10.36548/jiip.2023.2.004.
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Osteoarthritis (OA) of the knee is a common disorder that contributes to physical decline and activity limitation. Early OA diagnosis and treatment can stop the disease's progression. The assessment of a physician's visual examination is impartial, subject to different interpretations, and highly dependent on their level of experience. Therefore, a system that employs machine learning techniques to automatically determine the degree of knee OA has been proposed in this study. At first, a specifically created one stage YOLOv2 network is employed to estimate the size of the kneecap according to the distribution of knee joints in low contrast thermal images. To be more specific, the knee Kellgren-Lawrence (KL) grading assignment is ordinal; therefore, a harsher penalty is provided for misrepresentation with a larger gap between the anticipated and actual KL grade. A machine learning architecture is then constructed and extensive tests are performed to demonstrate how texture properties affect diagnostic performance. Thermal images are used to determine if they might be used to distinguish between radiographs of diseased and healthy knees. The outcomes of machine learning features and manually extracted features are compared. Finally, a stacked model that combines second-level machine learning with predictions of patellar texture and clinical characteristics is provided.
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Oei, Edwin H. G., Tijmen A. van Zadelhoff, Susanne M. Eijgenraam, Stefan Klein, Jukka Hirvasniemi, and Rianne A. van der Heijden. "3D MRI in Osteoarthritis." Seminars in Musculoskeletal Radiology 25, no. 03 (2021): 468–79. http://dx.doi.org/10.1055/s-0041-1730911.
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AbstractOsteoarthritis (OA) is among the top 10 burdensome diseases, with the knee the most affected joint. Magnetic resonance imaging (MRI) allows whole-knee assessment, making it ideally suited for imaging OA, considered a multitissue disease. Three-dimensional (3D) MRI enables the comprehensive assessment of OA, including quantitative morphometry of various joint tissues. Manual tissue segmentation on 3D MRI is challenging but may be overcome by advanced automated image analysis methods including artificial intelligence (AI). This review presents examples of the utility of 3D MRI for knee OA, focusing on the articular cartilage, bone, meniscus, synovium, and infrapatellar fat pad, and it highlights several applications of AI that facilitate segmentation, lesion detection, and disease classification.
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van, Tunen Joyce A. C., George Peat, Alessio Bricca, et al. "Association of osteoarthritis risk factors with knee and hip pain in a population-based sample of 29–59 year olds in Denmark: a cross-sectional analysis." BMC Musculoskeletal Disorders 19, no. 1 (2018): 300. https://doi.org/10.1186/s12891-018-2183-7.
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<strong>Background: </strong>This study aimed to a) describe the prevalence of knee and hip osteoarthritis risk factors in a population of 29–59 year old individuals, b) estimate the association between persistent knee/hip pain and osteoarthritis risk factors, and c) describe the prevalence of osteoarthritis risk factors, including specific biomechanical risk factors, in individuals with prolonged persistent knee or hip pain.<strong>Methods: </strong>Participants completed the "Early Detection and Prevention" pilot study questionnaire, including items on presence of knee/hip pain within the last month and osteoarthritis risk factors. Individuals reporting knee/hip problems completed a second questionnaire, including items about most problematic joint and specific biomechanical osteoarthritis risk factors. After describing the prevalence of persistent knee/hip pain and osteoarthritis risk factors among respondents stratified for sex and age, logistic regression was used to estimate the strength of associations between osteoarthritis risk factors and presence of knee/hip pain. The prevalence of prolonged persistent pain (i.e. knee/hip pain reported at both questionnaires) and osteoarthritis risk factors among respondents with prolonged persistent knee and hip pain, were described.<strong>Results: </strong>Two thousand six hundred sixty-one respondents completed the first survey. The one-month prevalence of persistent knee/hip pain was 27%. Previous knee/hip injury was associated with persistent knee/hip pain for both sexes in all age groups, while a family history of osteoarthritis was associated with persistent knee/hip pain in all age groups except for 29–39 year old men. A higher BMI was associated with persistent knee/hip pain in 40–59 year old women, and 50–59 year old men. Eight hundred sixty seven respondents completed the second questionnaire. Knee/hip injuries and surgeries were more common in individuals with prolonged persistent knee than hip pain.<strong>Conclusions: </strong>Knee/hip pain within the last month was frequent among individuals aged 29–59 years. Multiple known osteoarthritis risk factors were associated with presence of knee/hip pain. Joint injury and previous surgery were more common in individuals with knee than hip pain. The results support the notion that joint injury and overweight during early adulthood are signs of a trajectory towards symptomatic osteoarthritis later in life and may help earlier identification of groups at high risk of future symptomatic osteoarthritis.<strong>Trial registration: </strong> ClinicalTrials.gov (NCT02797392). Registered April 29,2016.
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Alexopoulos, A., J. Hirvasniemi, S. Klein, C. Donkervoort, E. H. G. Oei, and N. Tümer. "EARLY DETECTION OF KNEE OSTEOARTHRITIS USING DEEP LEARNING ON KNEE MRI." Osteoarthritis Imaging 3 (2023): 100112. http://dx.doi.org/10.1016/j.ostima.2023.100112.
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Nurfadhillah, Dea, Gunawan Santoso, Fatimah, Gatot Murti Wibowo, Darmini, and Nuryatno. "Effectiveness of Automatic Detection of Osteoarthritis using Convolutional Neural Network (CNN) Method with DenseNet201 on Digital Images of Knee Joint Radiography." E3S Web of Conferences 448 (2023): 02052. http://dx.doi.org/10.1051/e3sconf/202344802052.
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The manual detection of osteoarthritis using Kellgren Lawrence system depends on experience and agreement between doctors. The study was conducted to develop DenseNet201 to assist doctors in making a diagnosis of osteoarthritis grading. This study analyzes the accuracy; sensitivity; specificity; positive predictive value (PPV) and negative predictive value (NPV) of DenseNet201 in grading osteoarthritis and compares the classification results between DenseNet201 and radiologists in detecting osteoarthritis on knee joint images. This study is an applied experiment that compares the classification results of DenseNet201 and radiology specialists. Firstly, DenseNet201 is built with the MATLAB R2021a. Tests are carried out by measuring accuracy, sensitivity, specificity, PPV and NPV of 75 images of knee joint. Lastly, the data is analyzed using the Wilcoxon statistical test. The study has shown that the performance of DenseNet201 was good in detecting osteoarthritis, with accuracy value 91.84%; sensitivity value 76.61%; specificity value 94.32%; PPV 82.60% and NPV 94.32%. There was no significant difference between classification results using DenseNet201 and radiologist with a value (p>0.05) of 0.119. DenseNet201 can be considered as an alternative diagnostic tool for osteoarthritis with the condition that verification of the diagnostic decision still refers to the confirmation and justification of the radiologist.
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Kobayashi, M., S. Nakamura, R. Arai, et al. "319 “ULTRA-EARLY” DETECTION OF THE KNEE OSTEOARTHRITIS." Osteoarthritis and Cartilage 18 (October 2010): S141. http://dx.doi.org/10.1016/s1063-4584(10)60346-9.
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Adhitya, G., and Sivabalakrishnan M. "Knee osteoarthritis detection an AI driven severity estimation." IET Conference Proceedings 2024, no. 23 (2025): 112–17. https://doi.org/10.1049/icp.2024.4410.
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Singha, Raju, Chanchal Kumar Dalai, and Deblina Sarkar. "A study on evaluation of knee osteoarthritis with MRI and comparing it with CT scan, high resolution USG and conventional radiography." Asian Journal of Medical Sciences 12, no. 12 (2021): 120–25. http://dx.doi.org/10.3126/ajms.v12i12.39174.
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Background: Knee osteo-arthritis is widely prevalent in the elderly population in our society and associated with significant morbidity and poor quality of life. Early diagnosis of the condition can enable timely and proper care for the patients. Magnetic Resonance Imaging, CT Scan, Ultrasonography and plain radiography are the different modalities of imaging that are commonly used for detection and diagnosis of knee osteo-arthritis. Aims and Objectives: To find out the early osteoarthritic changes of knee by Magnetic Resonance Imaging and compare those findings with conventional radiography, high frequency USG and CT scan findings. Materials and Methods: Patients suffering from knee osteoarthritis (OA) as per American College of Rheumatology guideline criteria (n=56) underwent imaging of the knee using plain radiography, ultrasonography, CT scan and MRI. The imaging findings studied in the patients were joint space narrowing (JSN), meniscal abnormality, Baker’s cyst, cruciate ligament abnormality, knee effusion, subchondral cyst, and loose bodies. A comparison between radiography, CT scan and USG was done for the imaging findings with MRI as the reference standard. Z-test of proportionality was used to find statistically significant difference for the three imaging modalities. A P<0.05 was deemed statistically significant. Results: The mean age of the patients was 61 years (38 males). The tibiofemoral compartment was most commonly affected. CT scan was more sensitive than radiography in detecting sub-chondral cyst (P=0.018) and loose bodies (P=0.004). USG and MRI were equally sensitive in detecting knee effusion (P=0.22) and synovial thickening (P=0.10). CT scan and MRI were equally sensitive in detecting subchondral cyst (P=1.00) and loose bodies (P=0.22). Conclusion: While CT imaging was more sensitive for detection of subchondral cysts and loose bodies than conventional radiography, it was as sensitive as MRI in detecting these findings in the study group. Additional study is warranted to assess diagnostic performance of CT scan and MRI in the diagnosis and progression of knee OA.
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Singha, Raju, Chanchal Kumar Dalai, and Deblina Sarkar. "A study on evaluation of knee osteoarthritis with MRI and comparing it with CT scan, high resolution USG and conventional radiography." Asian Journal of Medical Sciences 12, no. 12 (2021): 120–25. https://doi.org/10.71152/ajms.v12i12.3665.
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Background: Knee osteo-arthritis is widely prevalent in the elderly population in our society and associated with significant morbidity and poor quality of life. Early diagnosis of the condition can enable timely and proper care for the patients. Magnetic Resonance Imaging, CT Scan, Ultrasonography and plain radiography are the different modalities of imaging that are commonly used for detection and diagnosis of knee osteo-arthritis. Aims and Objectives: To find out the early osteoarthritic changes of knee by Magnetic Resonance Imaging and compare those findings with conventional radiography, high frequency USG and CT scan findings. Materials and Methods: Patients suffering from knee osteoarthritis (OA) as per American College of Rheumatology guideline criteria (n=56) underwent imaging of the knee using plain radiography, ultrasonography, CT scan and MRI. The imaging findings studied in the patients were joint space narrowing (JSN), meniscal abnormality, Baker’s cyst, cruciate ligament abnormality, knee effusion, subchondral cyst, and loose bodies. A comparison between radiography, CT scan and USG was done for the imaging findings with MRI as the reference standard. Z-test of proportionality was used to find statistically significant difference for the three imaging modalities. A P<0.05 was deemed statistically significant. Results: The mean age of the patients was 61 years (38 males). The tibiofemoral compartment was most commonly affected. CT scan was more sensitive than radiography in detecting sub-chondral cyst (P=0.018) and loose bodies (P=0.004). USG and MRI were equally sensitive in detecting knee effusion (P=0.22) and synovial thickening (P=0.10). CT scan and MRI were equally sensitive in detecting subchondral cyst (P=1.00) and loose bodies (P=0.22). Conclusion: While CT imaging was more sensitive for detection of subchondral cysts and loose bodies than conventional radiography, it was as sensitive as MRI in detecting these findings in the study group. Additional study is warranted to assess diagnostic performance of CT scan and MRI in the diagnosis and progression of knee OA.
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Prastowo, Bayu, Matilda Novian Bai, Ni Nyoman Diah Prameswari, et al. "An overview of knee osteoarthritis severity in Pandanwangi, Malang." Journal of Community Empowerment for Health 7, no. 2 (2024): 105. http://dx.doi.org/10.22146/jcoemph.93385.
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Introduction: The global prevalence of knee osteoarthritis in older adults is expected to continue to increase until 2050. This increase was consistent with the prevalence rates in Malang. This progression is simultaneously controlled by Sustainable Development Goals. This service is a measure to support the government in the early detection of severity and education of knee osteoarthritis patients. Methods: The method was adopted through Community-Based Participatory Research at Puskesmas Pandanwangi, Malang. The respondents included 58 elderly people with symptoms of knee osteoarthritis. According to the Indonesian Rheumatology Association, the symptoms often experienced in Indonesia are pain, crepitus when moved, joint stiffness for more than 30 min, indications of enlargement of the knee area, tenderness of the bone edge, and no warmth in the synovium area. Results: The results of the average mapping show that respondents experienced knee osteoarthritis severity in the mild category or had a Laquesne index score of more than 1-4. Conclusion: The severity of osteoarthritis in Pandanwangi is influenced by age and body mass index. These respondents were educated on sitting up and down, stair step up, knee flexion, heel slide knee extension, and tight booster exercises to prevent severity at the next level. Exercise parameters were adjusted according to the respondent's condition for 8-12 weeks, 2-3 sessions per week.
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Maley, Deepak Kumar, Maheshwar Lakkireddy, Rakesh Ashok Khiyani, et al. "Comparative analysis of knee radiographs between weight bearing AP and Rosenberg PA flexion view for knee osteoarthrosis." Indian Journal of Orthopaedics Surgery 10, no. 1 (2024): 34–38. http://dx.doi.org/10.18231/j.ijos.2024.006.
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: Osteoarthritis of the knee is highly prevalent in our country and a significant cause of morbidity. An early diagnosis is of paramount importance to provide treatment and delay it’s progression.: To evaluate the role of Rosenberg PA flexion view for early diagnosis of osteoarthritis, the degree of articular degeneration and the advantages of these views using Kellgren-Lawrence classification (KL), Ahlback grading (AB) and International Knee Documentation Committee (IKDC). Rosenberg PA flexion view significantly increased the detection of medial and lateral joint space narrowing (p&#60;0.001) as compared to the standard weight bearing AP view alone. The severity of KL, Ahlback & IKDC scores increased significantly on Rosenberg view. The Rosenberg view increases the sensitivity of detection of JSN & the knee scores significantly & is an important tool for early diagnosis of OA.
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Vashishtha, Anuradha, and Anuja kumar Acharya. "An Overview of Medical Imaging Techniques for Knee Osteoarthritis Disease." Biomedical and Pharmacology Journal 14, no. 02 (2021): 903–19. http://dx.doi.org/10.13005/bpj/2192.
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Osteoarthritis is the most common form of “Arthritis & Joint disease”. Osteoarthritis (OA) is one of the fundamental causes of older and overweight individual’s sickness. It is the main cause of disability in adults. Mostly this disease occurs in people above 45 years of age, in which women suffer more as compared to men. it is basically damaged the Cartilage, because of which bones rub each other causing intense pain and inflammation. this gets thick and makes spurs at the edges. The knee Osteoarthritis is of 4 grades according to X-ray. The first 2 grade and 3rd grade can be recovered with the help of therapy and medications, while the 4th grade is necessary for knee replacement. The emerging Osteoarthritis management approach involves clinical evaluation & diagnostic imaging techniques. Within this research, we explore descriptively and objectively the various medical imaging methods used to diagnose and identify knee osteoarthritis. We study on the automatically detection of recovery rate of human disease and classify Osteoarthritis in the knee from medical images (like Magnetic Resonance image, CT scan, X-ray) from various medical image classification procedures. This paper provides a study that focuses on the various medical imaging methods used to determine osteoarthritis.
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Tang, Ling. "A study of the effectiveness of ultrasound imaging in the early detection of cartilage degeneration in the knee joint of athletes." Molecular & Cellular Biomechanics 22, no. 1 (2025): 980. https://doi.org/10.62617/mcb980.
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Knee injury is one of the common sports injuries, this paper aims to analyze the performance of ultrasound imaging technology in the assessment of early detection of athletes with knee pain, to provide a reliable assessment index for clinical diagnosis and treatment and an index for judging the efficacy. To analyze the causes of cartilage degeneration in athletes’ knee joints, and to evaluate and compare the ultrasound imaging method with the commonly used clinical assessment (Visual Analogue Scale (VAS) score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score) and imaging assessment (Digital Radiography(DR), Magnetic Resonance Imaging (MRI)) for knee osteoarthritis according to the advantageous performance of ultrasound imaging technology in the examination of knee joint diseases. The correlation between ultrasound assessment and VAS score and WOMAC score, as well as DR assessment and MRI assessment were obtained, respectively. To investigate the value of ultrasonography in the evaluation of patients with osteoarthritis of the knee. The ultrasound scores of the knee were positively correlated with the VAS scores and WOMAC scores, with correlation coefficients of 0.891 and 0.902, respectively. The correlation coefficients of ultrasound ratings with DR ratings and MRI ratings were 0.876 and 0.895, respectively (both > 0.75), which were good correlations.
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Slimani, S., A. Haddouche, I. Bencharif, and A. Ladjouze-Rezig. "Superiority of knee ultrasound over radiographs in osteophyte detection in knee osteoarthritis." Osteoarthritis and Cartilage 21 (April 2013): S197. http://dx.doi.org/10.1016/j.joca.2013.02.413.
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To, Anh Quan, Anh Huy Nguyen, Thi Huynh Nhu Le, Thi Yen Nhi Nguyen, Huyen Tran Le, and Phuoc Quy Nguyen. "STUDY ON THE ULTRASONOGRAPHIC AND RADIOGRAPHIC FINDINGS OF KNEE OSTEOARTHRITIS PATIENTS AT CAN THO UNIVERSITY OF MEDICINE AND PHARMACY HOSPITAL IN 2022-2023." Tạp chí Y Dược học Cần Thơ, no. 7 (May 20, 2024): 196–200. http://dx.doi.org/10.58490/ctump.2024i7.2979.
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Background: Knee osteoarthritis is a common disease in the group of bone and joint diseases. The incidence of the disease increases with age, commonly occurs in elderly patients or can also occur in young people. Diagnosis of knee osteoarthritis is mainly based on clinical symptoms combined with radiographs. Currently, ultrasound can be used to examine the damage that occurs in early-stage knee osteoarthritis. In addition, it has the ability to evaluate joint cartilage thickness, detect synovitis, joint effusion,... and abnormal features in other joint diseases of the knee, especially when there are no clinical manifestations or no damage on X-ray images. Objectives: To describe and compare ultrasonographic and radiographic findings in osteoarthritis-affected knee joints. Materials and Methods: A cross-sectional study was conducted on 50 patients and 62 knee joints diagnosed with osteoarthritis according to the American College of Rheumatology (ACR) 1991 criteria. General characteristics, radiographic findings, and ultrasonographic findings in osteoarthritis-affected knee joints of participants were collected at Can Tho University of Medicine and Pharmacy from May 2022 to April 2023. Results: Radiographs showed the most common finding was grade 1 osteophytes. The most common feature found on ultrasound was also grade 1 osteophytes. Medial condyle osteophytes and lateral condyle osteophytes: Grade 1 was the most common finding, with 53.2% and 58.1%. Medial tibial plateau osteophytes and lateral tibial plateau osteophytes: Grade 1 was the most common finding, with 54.8% and 50.0%. Medial compartment joint space narrowing: grade 1 was the most common (41.9%). Lateral compartment joint space narrowing was not found (grade 0) in 64.5%. In addition, ultrasound detected synovitis in patients with knee osteoarthritis, accounting for a fairly high rate of 87.1%. There was moderate agreement between osteophyte and joint space narrowing grading on ultrasound and radiographs. Conclusion: There was moderate agreement between osteophyte and joint space narrowing grading on ultrasound and radiographs. Ultrasound can also detect and evaluate synovitis that may not be seen on radiographs. We recommend using the ultrasonography atlas created for knee osteophyte detection in routine knee ultrasound.
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C D, USHA. "Smart Knee X-Ray Analysis for Osteoarthritis Diagnosis." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47779.
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Smart Knee X-Ray Analysis for Osteoarthritis: An Overview Diagnosis is a machine learning method designed to improve osteoarthritis identification. Millions of people worldwide suffer from osteoarthritis, a degenerative joint condition that causes pain, stiffness, and decreased mobility. In this study, preprocessing methods including data purification and feature extraction are used to evaluate knee X-ray pictures. Machine learning techniques CNN, SVM, and Random Forest are utilized to categorize patients as either normal or having osteoarthritis. By automating diagnostics, the technique improves accuracy and enables early detection. Deep learning is incorporated to guarantee consistent results and give medical professionals an intuitive interface. This strategy aims to enhance patient outcomes, encourage prompt intervention, and lessen long-term joint degeneration. Key words: computer vision, image processing, guidance system, and object recognition.
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Geeta R. Bharamagoudar, Malathi S. Y. ,. "A Predictive Modeling for Characterization and Grading of Knee Osteoarthritis Using Machine Learning Algorithms: A Study in Early Diagnosis and Prognosis." Journal of Electrical Systems 20, no. 5s (2024): 01–09. http://dx.doi.org/10.52783/jes.1828.
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Osteoarthritis (OA), particularly knee osteoarthritis, is the most prevalent form of arthritis, resulting in severe dis-ability for sufferers throughout the world. A Manual diagnosis, segmentation, and annotating joints of knee continue to be the most used procedure for diagnosing osteoarthritis (OA) in clinical settings, despite being laborious and highly susceptible to user variation. Several prediction models displayed prognostic ability in ways of predicting the possible onset of OA, the potential aggravation of OA, the prospective progression of pain and structural deterioration as well as the potential occurrence of total knee replacement (TKR). Apart from research gaps, techniques of machine learning continue to demonstrate enormous potential for challenging tasks e.g., initial knee OA detection and recognition of further disease events, also basicthings such as identifying innovative imaging features and establishing a novel measure of OA status. Future OA treatment discoveries may be aided by the continuous improvement of machine learning models.
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Aneeta, Shaista Shoukat, Ameet Kumar, Rubnawaz Baloch, and Vinod Kumar. "Quantitative MRI T2 relaxometry of knee joint in early detection of osteoarthritis." Journal of Fatima Jinnah Medical University 15, no. 3 (2022): 127–31. http://dx.doi.org/10.37018/bgcg4108.
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Background: Magnetic resonance imaging (MRI) T2 is an advance modality for the early diagnosis of osteoarthritis. This study was performed to determine the MRI T2 relaxometry value of knee joint in early detection of osteoarthritis among suspected cases.Patients and methods: This observational study was conducted at Department of Radiology, Jinnah Postgraduate Medical Centre (JPMC), Karachi, Pakistan from 20th September 2020 to 28th February 2021. All patients aged 20-60 years of either gender suspected of knee osteoarthritis were consecutively enrolled. Osteoarthritis was confirmed based on Kellgren & Lawrence (KL) radiographic grading of 2-5. MRI T2 relaxometry was performed in all patients.Results: Of 102 patients, there were 67 (65.7%) males and 35 (34.3%) females. Mean age was 43.72 ±14.01 years. KL grading showed that KL grade 0 observed in 29 (28.4%), grade I in 13 (12.7%), grade II in 25 (24.5%), grade III in 30 (29.4%), and grade IV in 5 (4.9%) patients. The frequency of osteoarthritis was found in 60 (58.8%) patients. Mean MRI T2 value was found to be 94.12 ±16.32. Mean MRI T2 value was found significantly higher in patients with KL grade IV (109.89 ±5.38) followed by KL grade III (107.35 ±3.24), KL grade II (97.72 ±14.65), KL grade I (89.54 ±13.69), and KL grade 0 (76.65 ±10.56). (p-value<0.001) The findings of ROC curve showed that AUC was found to be 0.911 (0.85-0.97) (p-value<0.001).Conclusion: MRI T2 relaxometry is highly recommended for the prediction of osteoarthritis in suspected cases.
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Leung, Kam Lun, Zongpan Li, Chen Huang, Xiuping Huang, and Siu Ngor Fu. "Validity and Reliability of Gait Speed and Knee Flexion Estimated by a Novel Vision-Based Smartphone Application." Sensors 24, no. 23 (2024): 7625. https://doi.org/10.3390/s24237625.
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Patients with knee osteoarthritis walk with reduced speed and knee flexion excursion in the early stance phase. A slow walking speed is also associated with falls in older adults. A novel vision-based smartphone application could potentially facilitate the early detection of knee osteoarthritis and fall prevention. This study aimed to test the validity and reliability of the app-captured gait speed and peak knee flexion during the initial stance phase of gait. Twenty adults (aged 23–68 years) walked at self-selected comfortable walking speeds while the gait speed and knee flexion were simultaneously measured using retroreflective sensors and Xsens motion trackers and the app in two separate sessions for validity and reliability tests. Pearson’s r correlation and Bland–Altman plots were used to examine the correlations and agreements between the sensor- and app-measured outcomes. One-sample t-tests were performed to examine whether systematic bias existed. The intraclass correlation coefficient (ICC) was calculated to assess the test–retest reliability of the app. Very high correlations were found between the sensor and app measurements for gait speed (r = 0.98, p < 0.001) and knee flexion (r = 0.91–0.92, all p < 0.001). No significant bias was detected for the final app version. The app also showed a good to excellent test–retest reliability for measuring the gait speed and peak knee flexion (ICC = 0.86–0.94). This vision-based smartphone application is valid and reliable for capturing the walking speed and knee flexion during the initial stance of gait, potentially aiding in the early detection of knee osteoarthritis and fall prevention in community living locations.
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Sohail, Muhammad, Muhammad Muzammil Azad, and Heung Soo Kim. "Knee osteoarthritis severity detection using deep inception transfer learning." Computers in Biology and Medicine 186 (March 2025): 109641. https://doi.org/10.1016/j.compbiomed.2024.109641.
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Frallonardo, Paola, Francesca Oliviero, Luca Peruzzo, et al. "Detection of Calcium Crystals in Knee Osteoarthritis Synovial Fluid." JCR: Journal of Clinical Rheumatology 22, no. 7 (2016): 369–71. http://dx.doi.org/10.1097/rhu.0000000000000416.
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Saleem, Mahrukh, Muhammad Shahid Farid, Saqib Saleem, and Muhammad Hassan Khan. "X-ray image analysis for automated knee osteoarthritis detection." Signal, Image and Video Processing 14, no. 6 (2020): 1079–87. http://dx.doi.org/10.1007/s11760-020-01645-z.
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Ike, Robert W., and Kenneth S. O'Rourke. "Detection of intraarticular abnormalities in osteoarthritis of the knee." Arthritis & Rheumatism 36, no. 10 (1993): 1353–63. http://dx.doi.org/10.1002/art.1780361005.
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Poonam, Km. "A Brief Analysis of T2 Mapping and Conventional MRI Techniques in Knee Osteoarthritis." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 173–80. https://doi.org/10.22214/ijraset.2025.67197.
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Background: Knee osteoarthritis (OA) exists as a common degenerative joint condition which advances cartilage breakdown and reduces functional capacity. The clinical healthcare system depends on Conventional Magnetic Resonance Imaging for structural analysis because T2 mapping gives procedural measures for early assessment of cartilage substance modification. T2 mapping will be evaluated through this study against conventional MRI techniques for assessing knee OA cartilage degeneration. Methods: The investigators performed a research study focused on people suspected of knee OA along with normal controls. The research participants received MRI examinations with both conventional sequences and T2 mapping. Evaluation of cartilage degeneration patterns across different regions relied on quantitative analysis of T2 values. Research statistics measured the importance of T2 value changes that appeared in cartilage affected by OA in comparison to normal cartilage. Results: The results from T2 mapping demonstrated increased T2 values appeared in cartilage tissue affected by OA especially in the patellofemoral and femoral condyle areas. The structural changes observed by conventional MRI received sufficient detection but the method failed to reveal early biochemical alterations. The mapping of T2 yielded better results in both precision and measurement ability to detect initial signs of cartilage deterioration. Conclusion: The detection and measurement of early cartilage degeneration in knee OA becomes more effective through T2 mapping as a diagnostic imaging tool. T2 mapping surpasses conventional MRI by detecting biochemical changes at an improved level which leads to better early detection and better treatment selection and disease management. Medical practitioners will benefit from T2 mapping integration with MRI protocols because it allows for timely interventions in knee OA management.
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Kambhamettu, Archit, Samantha Snyder, Maliheh Fakhar, et al. "VidSole: A Multimodal Dataset for Joint Kinetics Quantification and Disease Detection with Deep Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28142–50. https://doi.org/10.1609/aaai.v39i27.35033.
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Understanding internal joint loading is critical for diagnosing gait-related diseases such as knee osteoarthritis; however, current methods of measuring joint risk factors are time-consuming, expensive, and restricted to lab settings. In this paper, we enable the large-scale, cost-effective biomechanical analysis of joint loading via three key contributions: the development and deployment of novel instrumented insoles, the creation of a large multimodal biomechanics dataset (VidSole), and a baseline deep learning pipeline to predict internal joint loading factors. Our novel instrumented insole measures the tri-axial forces and moments across five high-pressure points under the foot. VidSole consists of the forces and moments measured by these insoles along with corresponding RGB video from two viewpoints, 3D body motion capture, and force plate data for over 2,600 trials of 52 diverse participants performing four fundamental activities of daily living (sit-to-stand, stand-to-sit, walking, and running). We feed the insole data and kinematic parameters extractable from video (i.e., pose, knee angle) into a deep learning pipeline consisting of an ensemble Gated Recurrent Unit (GRU) activity classifier followed by activity-specific Long Short Term Memory (LSTM) regression networks to estimate knee adduction moment (KAM), a biomechanical risk factor for knee osteoarthritis. The successful classification of activities at an accuracy of 99.02 percent and KAM estimation with mean absolute error (MAE) less than 0.5 percent*body weight*height, the current threshold for accurately detecting knee osteoarthritis with KAM, illustrates the usefulness of our dataset for future research and clinical settings.
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Velickovic, Z., S. Janjic, V. Bajec, et al. "SAT0572 CONSTRUCTIVE VALIDITY OF MUSKULOSKELETAL ULTRASOUND MEASUREMENT OF CARTILAGE THICKNESS IN PATIENTS WITH KNEE OSTEOARTHRITIS." Annals of the Rheumatic Diseases 79, Suppl 1 (2020): 1244.2–1245. http://dx.doi.org/10.1136/annrheumdis-2020-eular.3049.
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Background:Cartilage thickness is one important measure in describing both OA development and progression. Based on current knowledge, conventional radiography (CR) and magnetic resonance imaging (MRI) have not been demonstrated to be superior over one another. Because of disadvantages of MRI and CR neither can be use in routine daily clinical practice for follow up of OA patients. Diagnostic ultrasound assessment (US) of cartilage thickness offers an alternative measure as a clinically available and more cost-effective source of knee articular cartilage imaging.Objectives:Our objective was to determine the relationship between US and CR measures of femoral cartilage thickness in patients with knee osteoarthritis because systematic feature- and site-specific cross-comparison between this two methods is still missing in the current literature.Methods:120 patients with knee osteoarthritis (240 knees) are recruited for this study. The joint space width (JSW) and Kellgren and Lawrence (K&L) grade were measured using weight-bearing anteroposterior 30° knee semi-flexion knee radiography (with inclusion criteria K&L grade 1-4). Femoral cartilage thickness was measured three times in supine position and with a suprapatellar transverse scan with the knee in maximal flexion at the lateral condyle (LC), medial condyle (MC) and intercondylar notch (IN) by one rheumatologist and arithmetic mean is taken. Pain and functionality are measured with VAS pain scale, Womac, Lysholm and SF 36 score. The agreement between two methods was evaluated with Bland-Altman analysis.Results:We found a statistically significant low level of rank correlation between CR and US measurements of mean cartilage thickness; ρ (rho) values between modalities were low (0.263 and 0.273 depending on side (right/left), p=0.005 and p=0.007 respectively). In Bland – Altman analysis, US measurement showed bad agreement with CR. Presence or absence of US features of OA (effusion, synovial hypertrophy, osteophytes and popliteal cysts) didn’t influence on cartilage thickness assessed by US (p>0.05). For US assessment, we found correlation only between cartilage thickness and VAS pain scale (ρ (rho) -0.281, p=0.004). We didn’t found any statistically significant correlation between CR thickness measures and pain/functionality/HRQoL scores (p>0.05).Conclusion:These results suggest that ultrasound may be a useful clinical tool to assess relative cartilage thickness. However, the absolute validity of the ultrasound measure is called into question due to the larger CR-based thickness measures and low level of agreement according to Bland-Altman analysis. The use of ultrasound as a complementary imaging tool along with CR may enable more accurate and cost-effective detection, prognosis and follow-up of knee osteoarthritis in routine clinical practice.References:[1]Mehta N, Duryea J, Badger GJ, et al. Comparison of 2 Radiographic Techniques for Measurement of Tibiofemoral Joint Space Width.Orthop J Sports Med. 2017;5:2325967117728675.[2]Schmitz RJ, Wang HM, Polprasert DR, Kraft RA, Pietrosimone BG. Evaluation of knee cartilage thickness: A comparison between ultrasound and magnetic resonance imaging methods.Knee. 2017;24:217–223.[3]Podlipská J, Guermazi A, Lehenkari P, et al. Comparison of Diagnostic Performance of Semi-Quantitative Knee Ultrasound and Knee Radiography with MRI: Oulu Knee Osteoarthritis Study.Sci Rep. 2016;6:22365[4]Razek AA, El-Basyouni SR. Ultrasound of knee osteoarthritis: interobserver agreement and correlation with Western Ontario and McMaster Universities Osteoarthritis.Clin Rheumatol. 2016;35:997–1001.[5]Oo WM, Bo MT. Role of Ultrasonography in Knee Osteoarthritis.J Clin Rheumatol. 2016;22:324–329.Disclosure of Interests:None declared
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Majidi, Hadi, Fatemeh Niksolat, and Khatereh Anbari. "Comparing the Accuracy of Radiography and Sonography in Detection of Knee Osteoarthritis: A Diagnostic Study." Open Access Macedonian Journal of Medical Sciences 7, no. 23 (2019): 4015–18. http://dx.doi.org/10.3889/oamjms.2019.617.
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BACKGROUND: Knee osteoarthritis (OA) is the most common degenerative disorder occurring in elderly people. Radiography and sonography are convenient techniques to detect diverse pathological features of knee OA.
 BACKGROUND: Knee osteoarthritis (OA) is the most common degenerative disorder occurring in older people. Radiography and sonography are convenient techniques to detect diverse pathological features of knee OA.
 AIM: The aim of the present study was to evaluate the diagnostic efficacy of radiography and sonography in the detection of diverse features of knee OA.
 METHODS: In a prospective cross-sectional diagnostic accuracy study, 50 consecutive patients with suspected knee OA (40 women and 10 men, mean age 41.2 ± 6.1 years), referred to the rheumatology clinic of the Shohada Hospital of Khorramabad. All obtained magnetic resonance imaging (MRI), radiographic and sonography images were evaluated by two radiologists and rheumatologist with sufficient expertise in degenerative knee disorders. MRI has been considered as a gold standard test in evaluating other tests. The sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV) and accuracy with 95% confidence intervals of radiography and sonography in the diagnosis of knee OA were calculated.
 RESULTS: Prevalence of the marginal osteophyte, geode and decreased joint thickness were significantly higher in patients with age > 40 years compared to ≤ 40 years (P ˂ 0.05). The incidence of diverse features of knee OA was not significantly different in terms of the patient’s gender, except for decreased joint space. The specificity of radiography was higher than its sensitivity.
 CONCLUSION: Our study showed that both radiography and sonography are useful imaging modalities, especially to diagnosis the positive cases of knee OA. The specificity of radiography is higher than to its sensitivity for all pathological features of knee OA. The sensitivity of sonography to detect some features of knee OA such as decreased joint thickness is considerably higher than radiography.
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Slimani, S., A. Haddouche, I. Bencharif, and A. Ladjouze-Rezig. "SAT0521 Superiority of Knee Ultrasound over Radiographs in Osteophyte Detection in Knee Osteoarthritis." Annals of the Rheumatic Diseases 72, Suppl 3 (2013): A758.2—A758. http://dx.doi.org/10.1136/annrheumdis-2013-eular.2245.
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Teo, Jia Chern, Ismail Mohd Khairuddin, Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, and Wan Hasbullah Mohd Isa. "Automated Detection of Knee Cartilage Region in X-ray Image." MEKATRONIKA 4, no. 1 (2022): 104–9. http://dx.doi.org/10.15282/mekatronika.v4i1.8627.
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The prevalence of a symptomatic knee or osteoarthritis (OA) is approximately 9.6% in men and 18.0% in women over 60 years of age according to the OARSI 2016 report. Using early on-stage clinical qualitative assessments through means of X-ray scans, the cartilage health and degradation of an individual can be monitored through cartilage shape and surface over time. In this paper, we implement the application of transfer learning models such as InceptionV3, Xception and DenseNet201 for feature extraction of a rebalanced 1,000 knee X-ray images taken from Osteoarthritis Initiative (OAI) dataset with 5 classes graded 0–4 according to Kellgren-Lawrence grading split into a 70/15/15 training/validation/testing split. The features extracted are subsequently fed into machine learning classifiers, namely support vector machine (SVM). An average multiclass accuracy of 71.33% was achieved for hyperparameter fine-tuned DenseNet201-SVM model.
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Ponce, Y., O. Rillo, A. Brigante, O. Leonardi, E. Raad, and M. C. Lunic. "POS1486-HPR DETECTION OF ASSOCIATED FIBROMYALGIA IN PATIENTS WITH OSTEOARTHRITIS OF THE KNEE ACCORDING TO THE MULTIDIMENSIONAL HEALTH ASSESSMENT QUESTIONNAIRE/ FIBROMYALGIA ASSESSMENT SCREENING TOOLS (MDHAQ/FAST4)." Annals of the Rheumatic Diseases 81, Suppl 1 (2022): 1089.1–1089. http://dx.doi.org/10.1136/annrheumdis-2022-eular.4217.
Full textAbstract:
BackgroundOsteoarthritis is the most prevalent joint pathology. knee Osteoarthritis is one of the most frequent locations and has the greatest impact on the health of patients. It can be associated up to 11% with depression and 23% with neuropathic pain. It has also been reported that it can coincide with fibromyalgia (FM) between 6% and 22.83%. This difference may be attributed to some factors, e.g., sample size and/or socioeconomic status. Therefore, the clinical interpretation of symptoms in knee osteoarthritis may be overestimated by the presence of FM. Pincus T. et al. used the FAST4 to identify the coexistence of FM in patients with various rheumatic pathologies. The FAST4 consists of using the MDHAQ (symptom checklist, painful joint count, fatigue and pain). A FAST4 score ≥ 3 allows the association of FM to be considered. The authors demonstrated good concordance with the 2011 FM criteria according to the ACR (with a sensitivity of 70.4% and a specificity of 97.1%)ObjectivesTo detect the presence of associated FM in patients with early and established knee osteoarthritis employing the MDHAQ/FAST4MethodsPatients ≥18 years, of both sexes, from our community with early and established knee osteoarthritis (Luyten, FP 2018 and Altman, R 1986 Classification Criteria respectively). All answered the MDHAQ/FAST3-F questionnaire, in addition to the Patient Health Questionnaire (PHQ9) and the Neuropathic Pain 4 Questions (DN4). Depression was considered respectively with a score ≥3 of the PHQ9 and neuropathic pain with 4 points of the DN4Results100 patients (96 with a diagnosis of early knee osteoarthritis and 4 with established knee osteoarthritis [Luyten - ACR]) with a median age of 58 (RIC 16) and female sex were included 72%.We observed associated FM (FAST4-F) in 31% of patients (27 with early knee osteoarthritis and 4 with established knee osteoarthritis). Median VAS pain, patient and physician global assessment of the disease was higher in patients with FM (p<0.0001). Disease activity by RAPID 3 was low in 4%, moderate in 38% and high in 58% of patients. The cohort had a median pain score of 60 (RIC 60) with absence of neuropathic pain (DN4) in 90% of patients. Depression (PHQ9) was observed in 29% of cases.OA evolution time in months in patients with FM 48 (RIC 12) vs. 36 (RIC 24) without FM (p< 0.0001).Binary logistic regression was performed. In the bivariate analysis, it was observed that the presence of early OA by Luyten’s criteria (p=0.002), the presence of depression by PHQ9 (p=0.001), patients of white ethnicity (p=0.03), sobrepeso (p=0.002), low RAPID 3 disease activity (p=0.001), kellgren and lawrence grades II and III (p=0.001), were significant in the bivariate model, although none were statistically significant in the multivariate modelConclusionThe evaluation of knee osteoarthritis can be complicated in those patients who coexist with FM. In our study 31% met criteria for this entity according to MDHAQ/FAST4. We consider this questionnaire to be single and practical for detecting associated FM. The results obtained give hierarchy to the use of PROMs (Patient Reported Outcomes Measures) that can recognize clues of associated diseases such as FM and/or depression. And thus be able to establish a timely and appropriate treatment when these pathologies are overlapping, in order to change the patient’s prognosis and improve their quality of lifeReferences[1]Pincus T, Schmukler J, Castrejon I. Patient questionnaires in osteoarthritis: what patients teach doctors about their osteoarthritis on a multidimensional health assessment questionnaire (MDHAQ) in clinical trials and clinical care. Clin Exp Rheumatol 2019; 37(Suppl. 120): S100-S111.[2]Gibson K, Castrejon I, Descallar J, et al. Fibromyalgia Assessment Screening Tool: Clues to Fibromyalgia on a Multidimensional Health Assessment Questionnaire for Routine Care. J Rheumatol. 2020;47(5):761-69AcknowledgementsUnidad de investigación de la Sociedad Argentina de Reumatología(UNISAR)Disclosure of InterestsNone declared
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Ignatenko, Grigory A., Natalia A. Reznichenko, Pavel N. Fedulichev, Eduard A. Maylyan, and Zaira F. Kharaeva. "Polymorphisms of genes of interleukin-6 and alpha-1 chain of collagen type 1 in postmenopausal women with knee osteoarthritis." Medical academic journal 23, no. 3 (2024): 31–40. http://dx.doi.org/10.17816/maj375358.
Full textAbstract:
BACKGROUND: To date in the Russian Federation insufficient attention has been paid to the study of IL6 and COL1A1 gene polymorphisms role in the development of knee osteoarthritis. And the results of the single carried out to date studies, devoted to the research of polymorphic variants of the above genes influence on the osteoarthritis development, are insufficient for substantiated conclusions.
 AIM: To study the frequency of alleles and genotypes of the IL6 gene rs1800795 polymorphism and COL1A1 gene rs1107946 and rs1800012 polymorphisms in postmenopausal women with knee osteoarthritis.
 MATERIALS AND METHODS: The results of 157 postmenopausal women survey with knee osteoarthritis were selected and analyzed. The control group consisted of 326 women of the same age without signs of joint disease. The study of polymorphisms rs1800795, rs1107946 and rs1800012 was performed by real-time polymerase chain reaction.
 RESULTS: The conducted studies showed that in the general group of examined women the frequency of all three studied polymorphisms genotypes registration corresponded to the Hardy-Weinberg law. An uneven (p = 0.043) distribution of rs1800795 polymorphism genotypes was found in the group of women with osteoarthritis and in the control group in the study of the IL6 gene polymorphic variants frequency detection. This difference was due to more frequent GG genotype registration of the above polymorphism (odds ratio = 1.75; 95% confidence interval: 1.12–2.72; p = 0.021) among women with knee osteoarthritis. Associations of rs1107946 and rs1800012 COL1A1 gene polymorphisms were not found (p 0.05).
 CONCLUSIONS: An association between GG genotype of the IL6 gene rs1800795 polymorphism and knee osteoarthritis in postmenopausal women has been established. Genotypes and alleles of COL1A1 gene rs1107946 and rs1800012 polymorphisms were not associated with joint disease.