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Relevant bibliographies by topics / Knee osteoarthritis detection

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Academic literature on the topic 'Knee osteoarthritis detection'

Author: Grafiati

Published: 7 June 2025

Last updated: 26 June 2025

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Journal articles on the topic "Knee osteoarthritis detection"

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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2

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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3

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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4

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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5

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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Abstract:

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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8

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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9

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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Book chapters on the topic "Knee osteoarthritis detection"

1

Garg, Aditi, Sahil Suryavanshi, Jimmi James, and Shilpa Srivastava. "Knee-Osteoarthritis Detection Using Deep Learning." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3180-0_6.

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Hossain, Md Belayet, Belinda Pingguan-Murphy, Hum Yan Chai, et al. "Improved Ultrasound Imaging for Knee Osteoarthritis Detection." In Lecture Notes in Bioengineering. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-540-2_1.

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Patil, Pradnya, Rushin Gala, Meeti Shah, and Purva Salvi. "ARTHRO—Knee Osteoarthritis Detection Using Deep Learning." In Data Science and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7820-5_15.

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Deoghare, Prathamesh, Yash Dhadbale, Shreyas Chore, Mrunalini bhandarkar, M. T. Kolte, and N. B. Chopade. "Knee Osteoarthritis Detection Using Machine Learning Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9112-5_22.

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5

Brahim, Abdelbasset, Rabia Riad, and Rachid Jennane. "Knee Osteoarthritis Detection Using Power Spectral Density: Data from the OsteoArthritis Initiative." In Computer Analysis of Images and Patterns. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29891-3_42.

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6

Golshan, Farshad, Yan Chai Hum, Belinda Pingguan-Murphy, and Khin Wee Lai. "Vibroarthrography Difference Between Left and Right Knee for Osteoarthritis Detection." In IFMBE Proceedings. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7554-4_50.

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Hegde, Anjani, Rishma Mary George, and H. D. Ranjith. "Detection and Classification of Knee Osteoarthritis Using Texture Descriptor Algorithms." In Intelligent Interactive Multimedia Systems for E-Healthcare Applications. Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003282112-10.

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8

Kadu, Rahul Nandkumar, Sunil N. Pawar, and Shakil A. Shaikh. "A Comprehensive Review on Advances in Detection of Knee Osteoarthritis." In Multi-Strategy Learning Environment. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1488-9_24.

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9

Gornale, Shivanand S., Pooja U. Patravali, and Prakash S. Hiremath. "Osteoarthritis Detection in Knee Radiographic Images Using Multiresolution Wavelet Filters." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0493-5_4.

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Bolegave, Pratik, Abhijeet Sonmale, and S. S. Vasekar. "Techniques for Knee Osteoarthritis Detection and Severity Grading Using Deep Learning." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6684-0_24.

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Conference papers on the topic "Knee osteoarthritis detection"

1

Abhishek, Abhishek, Ranjit Singh, and Navneet Kaur. "Radiographic knee osteoarthritis detection using liquid neural networks." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11034969.

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Gomathi, V., S. Vasunthra, K. Midhun Kumar, S. P. Preethi, and A. Sureka. "Knee Osteoarthritis Detection and Staging using Ordinal Classification with ResNet50." In 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2024. https://doi.org/10.1109/smart63812.2024.10882490.

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Balaji, S., R. Sathishkumar, S. V. Dhinesh, S. Vasanthakumar, and R. Jayavarman. "Knee Osteoarthritis Based X- Ray Images Detection and Classification Using RCNN." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894561.

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M, Arunkumar, Aarthi R, and Selvagananathv J. "Deep Learning with 3D CNN for Knee Osteoarthritis Detection and Classification." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894238.

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Shirisha, N., K. Shilpa, CH Meghana, and Mohammed Abdul Rahman. "Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201." In 2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC). IEEE, 2025. https://doi.org/10.1109/isacc65211.2025.10969337.

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Rifat, Saima, and Sheena Mohammed. "Detection of Knee Osteoarthritis Using the Xception Model and Voting Classifier." In 2024 2nd International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR). IEEE, 2024. https://doi.org/10.1109/icaitpr63242.2024.10960115.

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Thangamani, R., M. Vimaladevi, S. Maheswaran, G. Sreeka, A. Srinithi, and S. Aswin. "Enhancing Knee Osteoarthritis Detection and Severity Grading Using Deep Learning and DenseNet169." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724058.

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8

Roca-Ginés, Alberto, Juan Antonio Romero-Martín, Pilar Castellote-Huguet, et al. "Multiparametric Analysis in Knee MRI for an Early Detection of Osteoarthritis Biomarkers." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782340.

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Sivakumar, C., Kalagatoori Deepti, Rawi Sai Revanth, Mangala Gowthami, and C. Sravana Geethika. "Implementation of Knee Osteoarthritis Detection and Severity Prediction Using Convolution Neural Network." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716938.

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Kumar, P. Ashok, Rasala Rohini, Nandyala Yashwanth Reddy, Vadapalli Murali Krishna, and Ravi Bolimera. "Knee Osteoarthritis Detection Using an Improved CenterNet With Pixel-Wise Voting Scheme." In 2024 International BIT Conference (BITCON). IEEE, 2024. https://doi.org/10.1109/bitcon63716.2024.10984566.

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