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Academic literature on the topic 'Grading knee OA'
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Journal articles on the topic "Grading knee OA"
Phalswal, Uma, Vandna Pandey, Ashok Kumar, and Abhay Elhence. "Correlation of Ahlback grading and knee society score in patients with moderate to severe osteoarthritis of the knee." International Journal of Research in Orthopaedics 7, no. 3 (April 26, 2021): 606. http://dx.doi.org/10.18203/issn.2455-4510.intjresorthop20211617.
Full textOikonomidis, Stergios A., Yannis V. Simos, Ioannis K. Toliopoulos, Ioannis I. Verginadis, Alexandros S. Oikonomidis, Vasilios N. Ragos, Spyridon Ch Karkabounas, Angelos M. Evangelou, and Dimitrios Peschos. "OXIDATIVE STRESS INCIDENCE ON THE SEVERITY OF KNEE OSTEOARTHRITIS." Journal of Musculoskeletal Research 20, no. 02 (June 2017): 1750008. http://dx.doi.org/10.1142/s0218957717500087.
Full textNaorem, Ajit Singh, Jugindro Singh Ningthoujam, K. Wangjam, and RK Rajesh. "Does Radiologic Grading Predict Severity of Osteo-arthritis Knee." Indian Journal of Physical Medicine and Rehabilitation 27, no. 3 (2016): 73–77. http://dx.doi.org/10.5005/ijopmr-27-3-73.
Full textLi, Bei, Yi-Li Zhang, and Shou-Yi Yu. "Synovial Fluid Eotaxin-1 Levels May Reflect Disease Progression in Primary Knee Osteoarthritis Among Elderly Han Chinese: A Cross-Sectional Study." CARTILAGE 10, no. 4 (March 22, 2018): 408–16. http://dx.doi.org/10.1177/1947603518764280.
Full textHariyanto, Hori, John Butar Butar, Gatot Susilo Lawrence, FX Budhianto Suhadi, and Andi Husni Tanra. "Association Between Plasma Beta-endorphin and WOMAC Score in Female Patients with Knee Osteoarthritis." Indonesian Biomedical Journal 4, no. 2 (August 1, 2012): 107. http://dx.doi.org/10.18585/inabj.v4i2.169.
Full textWidhiyanto, Lukas, Andre Triadi Desnantyo, Lilik Djuari, and Maynura Kharismansha. "CORRELATION BETWEEN KNEE OSTEOARTHRITIS (OA) GRADE AND BODY MASS INDEX (BMI) IN OUTPATIENTS OF ORTHOPAEDIC AND TRAUMATOLOGY DEPARTMENT RSUD DR. SOETOMO." (JOINTS) Journal Orthopaedi and Traumatology Surabaya 6, no. 2 (December 9, 2019): 71. http://dx.doi.org/10.20473/joints.v6i2.2017.71-79.
Full textTiulpin, Aleksei, and Simo Saarakkala. "Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs Using Deep Convolutional Neural Networks." Diagnostics 10, no. 11 (November 10, 2020): 932. http://dx.doi.org/10.3390/diagnostics10110932.
Full textCubukcu, Duygu, Ayse Sarsan, and Hakan Alkan. "Relationships between Pain, Function and Radiographic Findings in Osteoarthritis of the Knee: A Cross-Sectional Study." Arthritis 2012 (November 19, 2012): 1–5. http://dx.doi.org/10.1155/2012/984060.
Full textZhao, Zhe, Enqi Li, Qing Cao, Jie Sun, and Baotong Ma. "Endothelin-1 concentrations are correlated with the severity of knee osteoarthritis." Journal of Investigative Medicine 64, no. 4 (February 25, 2016): 872–74. http://dx.doi.org/10.1136/jim-2015-000030.
Full textMoniruzzaman, Mohammad, Muhammad Alamgir Mandal, Md Ariful Islam, Md Ashraful Haque, Md Ahsan Ullah, Syed Mozaffar Ahmed, Md Habibur Rahman, Md Zakir Hossain, and Md Masudur Rahman. "A study on knee osteoarthritis in physical medicine and rehabilitation department, Rangpur Medical College." KYAMC Journal 8, no. 2 (February 19, 2018): 18–23. http://dx.doi.org/10.3329/kyamcj.v8i2.35698.
Full textDissertations / Theses on the topic "Grading knee OA"
Sen, Koktas Nigar. Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609347/index.pdf.
Full texttransformed knowledge&rsquo
of these experts. This study presents a clinical decision support system for the detecting and scoring of a knee disorder, namely, Osteoarthritis (OA). Data used for training and recognition is mainly obtained through Computerized Gait Analysis software. Sociodemographic and disease characteristics such as age, body mass index and pain level are also included in decision making. Subjects are allocated into four OA-severity categories, formed in accordance with the Kellgren-Lawrence scale: &ldquo
Normal&rdquo
, &ldquo
Mild&rdquo
, &ldquo
Moderate&rdquo
, and &ldquo
Severe&rdquo
. Different types of classifiers are combined to incorporate the different types of data and to make the best advantages of different classifiers for better accuracy. A decision tree is developed with Multilayer Perceptrons (MLP) at the leaves. This gives an opportunity to use neural networks to extract hidden (i.e., implicit) knowledge in gait measurements and use it back into the explicit form of the decision trees for reasoning. Individual feature selection is applied using the Mahalanobis Distance measure and most discriminatory features are used for each expert MLP. Significant knowledge about clinical recognition of the OA is derived by feature selection process. The final system is tested with test set and a success rate of about 80% is achieved on the average.