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Academic literature on the topic 'Kellgren- Lawrence classification grades'
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Journal articles on the topic "Kellgren- Lawrence classification grades"
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Ozaki, Yusuke, Ryota Hara, Kensuke Okamura, et al. "Correlation between varus-type knee osteoarthritis severity and hindfoot alignment: Analysis of radiographs in the long-leg weight-bearing anteroposterior view." PLOS One 20, no. 6 (2025): e0324974. https://doi.org/10.1371/journal.pone.0324974.
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Background In knee osteoarthritis, the subtalar joint undergoes valgus and varus contractions to compensate for deformities in the knee joint. In this cross-sectional study, we investigated the relationship between varus-type knee osteoarthritis severity and hindfoot alignment severity by concurrently assessing varus-type knee osteoarthritis severity and hindfoot alignment using radiographs in the long-leg weight-bearing anteroposterior view. Patients and methods A total of 114 patients with knee osteoarthritis graded Kellgren–Lawrence II or higher (128 knees) and 30 healthy controls (31 knees) underwent long-leg weight-bearing anteroposterior imaging for 1 year. Four angles were measured on radiographs in the long-leg weight-bearing anteroposterior view: the femorotibial angle; tibial calcaneal angle; tibial anterior surface angle; and talocrural joint angle between the tibial plafond and talar dome on weight-bearing. Group comparisons were conducted for each Kellgren–Lawrence classification, which was used to classify the severity of knee osteoarthritis at each measured angle. One-way analysis of variance was used to test the results. Results The mean tibial calcaneal angles were 9.7°, 11.3°, 8.8°, and 9.8° in controls and in patients with Kellgren–Lawrence grades II, III, and IV, respectively (p < 0.05). The mean femorotibial angles were 175.6°, 176.8°, 180.3°, and 186.2° in controls and in patients with Kellgren–Lawrence grades II, III, and IV, respectively (p < 0.05). On weight-bearing, the tibial anterior surface angle and the talocrural joint angle between the tibial plafond and talar dome varied according to severity level. Conclusion In varus-type knee osteoarthritis cases, defined in accordance with the Kellgren–Lawrence classification, hindfoot alignment leaned toward valgus. As the severity of knee osteoarthritis progressed, the valgus of the hindfoot alignment reduced. While future longitudinal analyses are necessary, these observations indicate both potential compensatory changes and their limitations in varus-type knee osteoarthritis.
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Moon, Ki-Ho. "New View on the Initial Development Site and Radiographic Classification System of Osteoarthritis of the Knee Based on Radiographic Analysis." International Journal of Biomedical Science 8, no. 4 (2012): 233–43. http://dx.doi.org/10.59566/ijbs.2012.8233.
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ntroduction: Radiographic pathology of severe osteoarthritis of the knee (OAK) such as severe osteophyte at tibial spine (TS), compartment narrowing, marginal osteophyte, and subchondral sclerosis is well known. Kellgren-Lawrence grading system, which is widely used to diagnose OAK, describes narrowing-marginal osteophyte in 4-grades but uses osteophyte at TS only as evidence of OAK without detailed-grading. However, kinematically the knee employs medial TS as an axis while medial and lateral compartments carry the load, suggesting that early OAK would occur sooner at TS than at compartment. Then, Kellgren-Lawrence system may be inadequate to diagnose early-stage OAK manifested as a subtle osteophyte at TS without narrowing-marginal osteophyte. This undiagnosed-OAK will deteriorate becoming a contributing factor in an increasing incidence of OAK. Methods: This study developed a radiographic OAK-marker based on both osteophyte at TS and compartment narrowing-marginal osteophyte and graded as normal, mild, moderate, and severe. With this marker, both knee radiographs of 1,728 patients with knee pain were analyzed.Results: Among 611 early-stage mild OAK, 562 or 92% started at TS and 49 or 8% at compartment. It suggests the initial development site of OAK, helping develop new site-specific radiographic classification system of OAK accurately to diagnose all severity of OAK at early, intermediate, or late-stage. It showed that Kellgren-Lawrence system missed 92.0% of early-stage mild OAK from diagnosis. Conclusions: A subtle osteophyte at TS is the earliest radiographic sign of OAK. A new radiographic classification system of OAK was suggested for accurate diagnosis of all OAK in severity and at stage.
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Mahum, Rabbia, Saeed Ur Rehman, Talha Meraj, et al. "A Novel Hybrid Approach Based on Deep CNN Features to Detect Knee Osteoarthritis." Sensors 21, no. 18 (2021): 6189. http://dx.doi.org/10.3390/s21186189.
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In the recent era, various diseases have severely affected the lifestyle of individuals, especially adults. Among these, bone diseases, including Knee Osteoarthritis (KOA), have a great impact on quality of life. KOA is a knee joint problem mainly produced due to decreased Articular Cartilage between femur and tibia bones, producing severe joint pain, effusion, joint movement constraints and gait anomalies. To address these issues, this study presents a novel KOA detection at early stages using deep learning-based feature extraction and classification. Firstly, the input X-ray images are preprocessed, and then the Region of Interest (ROI) is extracted through segmentation. Secondly, features are extracted from preprocessed X-ray images containing knee joint space width using hybrid feature descriptors such as Convolutional Neural Network (CNN) through Local Binary Patterns (LBP) and CNN using Histogram of oriented gradient (HOG). Low-level features are computed by HOG, while texture features are computed employing the LBP descriptor. Lastly, multi-class classifiers, that is, Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbour (KNN), are used for the classification of KOA according to the Kellgren–Lawrence (KL) system. The Kellgren–Lawrence system consists of Grade I, Grade II, Grade III, and Grade IV. Experimental evaluation is performed on various combinations of the proposed framework. The experimental results show that the HOG features descriptor provides approximately 97% accuracy for the early detection and classification of KOA for all four grades of KL.
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Umesh, Hengaju. "Classification of Knee Osteoarthritis using CNN." Advancement in Image Processing and Pattern Recognition 5, no. 1 (2022): 1–14. https://doi.org/10.5281/zenodo.6491218.
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<em>Knee osteoarthritis (OA) is a joint disease which is globally common in elder people. It is typically the result of wear and tear and progressive loss of articular cartilage. It has no cure. Despite of its high prevalence, there is a lack of diagnostic tools and approaches that detects and classifies the different stages of Knee OA severalties with better precision. This paper presents the approaches to automatically quantify the severity of knee OA using X-ray images. Two of the CNN classifiers namely, VGG-15 and ResNet-32 have been used for classifying the knee OA severity into one of the 5 Kellgren-Lawrence classification grades (normal, doubtful, mild, moderate and severe). These models have been trained using loss function: ‘categorical cross entropy’ and optimizer ‘Adam’. The datasets used in this work has been collected from Bhaktapur Hospital. About 350 X-ray images were collected and manually classified into their KL grades and then they were used for testing as well as training the models. The test results shows that the accuracy of classifying knee OA severities with VGG-16 and ResNet-32 were 59% and 57% respectively. It seemed that the accuracy of VGG-16 model is better than ResNet-32 in quantifying knee OA severity. </em>
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Yamada, Junichi, Koji Akeda, Norihiko Takegami, Tatsuhiko Fujiwara, Akinobu Nishimura, and Akihiro Sudo. "Novel elemental grading system for radiographic lumbar spondylosis in a population based-cohort study of a Japanese mountain village." PLOS ONE 17, no. 6 (2022): e0270282. http://dx.doi.org/10.1371/journal.pone.0270282.
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Purpose Lumbar radiography is a primary screening tool for lumbar spondylosis (LS). Kellgren-Lawrence (KL) classification is widely used to evaluate LS; however, it cannot individually evaluate each radiographic feature. The purpose of this study was to 1) evaluate radiographic LS using a novel elemental grading system and 2) investigate the relationship between the grades of radiographic LS and low back pain (LBP) in a population-based cohort study. Methods A total of 260 (75 men, 185 women; mean age, 71.5 ± 8.7 years) participants were included in this study. Participants were divided into two groups according to the presence of LBP (LBP- and LBP+ groups). Radiographic features, including osteophyte (OP), disc height narrowing (DHN), vertebral sclerosis (VS), and spondylolisthesis (SL), were classified between grades of 0–2 grades according to the extent of radiographic changes. The sum of grades at each intervertebral level was designated as the intervertebral grade (IG). Results Intra- and inter-observer reliability (kappa coefficient) of OP, DHN, VS, and SL were 0.82–0.92. OP, DHN, VS, and IG grades were significantly higher in the LBP+ group than in the LBP- group. There were no significant differences in KL grades between the LBP- and LBP+ groups. Logistic regression analysis demonstrated that VS grade was a significant independent factor associated with LBP. Conclusion The novel elemental grading system of LS would reflect LBP more accurately than the KL classification by individually evaluating each radiographic feature.
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Bhateja, Vikrant, Yatndeep Dubey, Navaneet Maurya, et al. "Ensemble CNN Model for Computer-Aided Knee Osteoarthritis Diagnosis." International Journal of Service Science, Management, Engineering, and Technology 15, no. 1 (2024): 1–17. http://dx.doi.org/10.4018/ijssmet.349913.
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Osteoarthritis (OA) is a multifaceted ailment posing challenges in its diagnosis and treatment due to the intricate nature of the disease. Particularly, Knee Osteoarthritis (KOA) significantly impacts the knee joint, manifesting through symptoms such as pain, stiffness, and limited movement. Despite its prevalence and debilitating effects, early detection of KOA remains elusive, often hindered by subjective diagnostic methods and the absence of reliable biomarkers. This research aims to address these challenges by leveraging deep learning techniques and ensemble methodologies for accurate KOA classification using knee X-ray images. This paper utilized a dataset sourced from the Osteoarthritis Initiative (OAI), comprising a large collection of knee X-ray images graded according to the Kellgren-Lawrence (KL) grading system. The proposed design methodology involves preprocessing the input X-ray images and training multiple pre-trained Convolutional Neural Network (CNN) models, including ResNet50, InceptionResNetV2, and Xception to classify KOA severity grades. Additionally, this work introduced an ensemble model by combining predictions from these base models to improve overall performance of the Computer-Aided Diagnosis (CAD) system. The obtained results demonstrate the effectiveness of the ensemble approach, outperforming individual algorithms in terms of accuracy, precision, recall, F1-score, and balanced accuracy. However, challenges persist in accurately distinguishing between adjacent KL grades, particularly grades#1 and #2, highlighting the need for further refinement. Notably, the proposed CAD model showcases superior predictive accuracy compared to various state-of-art methods, offering a promising avenue for early KOA diagnosis and personalized treatment strategies.
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Boldbayar, Tuvshinjargal, Baatarjav Sosor, Otgonbayar Maidar, Sergelen Orgoi, and Munkhbaatar Dagvasumberel. "Mid-Term Results of High Tibial Osteotomy Regarding From Grades of Knee Osteoarthritis." Central Asian Journal of Medical Sciences 8, no. 2 (2022): 90–101. http://dx.doi.org/10.24079/cajms.2022.06.003.
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Objectives: The number of high tibial osteotomy (HTO) has significantly increased in Mongolia, however, researchers are still debating about the impact of severity of knee osteoarthritis on the outcome of high tibial osteotomy. The purpose of our study is to report the mid-term results of HTO for knee osteoarthritis according to the Kellgren Lawrence classification. Methods: A total of 100 patients who underwent high tibial osteotomy for knee osteoarthritis from October 2019 to June 2020 at the Joint Center of the First Central Hospital of Mongolia participated in this study. Outcome evaluation of the participants was accomplished at baseline, at 2 months, 6 months, 8 months and 18 months post-operatively. Results: Lateral closing wedge HTO was performed in 54.2 % of patients who had 1st grade deformity and 55.9 % of patients who had 2nd grade deformity. On the other hand, medial opening wedge HTO was performed in 3rd grade patients compared to other grades. WBL was 11.43 ± 8.22 at preoperative, and increased to 56.31 ± 4.52 after 12 months of the surgery in 3rd grade. The total WBL was improved from 20.54 ± 12.57 to 57.24 ± 3.69 after two months of surgery and 57.89 ± 4.17 after 12 months. Conclusion: Our study showed that the severity of knee osteoarthritis had impact on outcome of high tibial osteotomy.
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Tsukamoto, Hiroaki, Kimio Saito, Hidetomo Saito, et al. "A Novel Classification of Coronal Plane Knee Joint Instability Using Nine-Axis Inertial Measurement Units in Patients with Medial Knee Osteoarthritis." Sensors 23, no. 5 (2023): 2797. http://dx.doi.org/10.3390/s23052797.
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The purpose of this study was to propose a novel classification of varus thrust based on gait analysis with inertial motion sensor units (IMUs) in patients with medial knee osteoarthritis (MKOA). We investigated thigh and shank acceleration using a nine-axis IMU in 69 knees with MKOA and 24 (control) knees. We classified varus thrust into four phenotypes according to the relative medial–lateral acceleration vector patterns of the thigh and shank segments: pattern A (thigh medial, shank medial), pattern B (medial, lateral), pattern C (lateral, medial), and pattern D (lateral, lateral). Quantitative varus thrust was calculated using an extended Kalman filter-based algorithm. We compared the differences between our proposed IMU classification and the Kellgren–Lawrence (KL) grades for quantitative varus thrust and visible varus thrust. Most of the varus thrust was not visually perceptible in early-stage OA. In advanced MKOA, increased proportions of patterns C and D with lateral thigh acceleration were observed. Quantitative varus thrust was significantly increased stepwise from patterns A to D. This novel IMU classification has better clinical utility due to its ability to detect subtle kinematic changes that cannot be captured with conventional motion analysis even in the early stage of MKOA.
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Mohammed, Abdul Sami, Ahmed Abul Hasanaath, Ghazanfar Latif, and Abul Bashar. "Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images." Diagnostics 13, no. 8 (2023): 1380. http://dx.doi.org/10.3390/diagnostics13081380.
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One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren–Lawrence (KL) system. This requires the physician’s expertise, suitable experience, and a lot of time, and even after that the diagnosis can be prone to errors. Therefore, researchers in the ML/DL domain have employed the capabilities of deep neural network (DNN) models to identify and classify KOA images in an automated, faster, and accurate manner. To this end, we propose the application of six pretrained DNN models, namely, VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121 for KOA diagnosis using images obtained from the Osteoarthritis Initiative (OAI) dataset. More specifically, we perform two types of classification, namely, a binary classification, which detects the presence or absence of KOA and secondly, classifying the severity of KOA in a three-class classification. For a comparative analysis, we experiment on three datasets (Dataset I, Dataset II, and Dataset III) with five, two, and three classes of KOA images, respectively. We achieved maximum classification accuracies of 69%, 83%, and 89%, respectively, with the ResNet101 DNN model. Our results show an improved performance from the existing work in the literature.
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Ishii, Yoshinori, Hideo Noguchi, Junko Sato, Hana Ishii, Ryo Ishii, and Shin-ichi Toyabe. "Knee Osteoarthritis Grade Does Not Correlate with Quadriceps Muscle Strength or Bone Properties of the Calcaneus in Men Aged 80 Years or More Who Can Walk Independently." International Journal of Environmental Research and Public Health 17, no. 5 (2020): 1709. http://dx.doi.org/10.3390/ijerph17051709.
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Purpose: Muscle weakness and bone deterioration in the elderly are related to falls and fractures, resulting in decreased mobility. Knee osteoarthritis also may contribute to falls and fractures and thereby affect mortality rates. The Kellgren–Lawrence (KL) classification is widely used in the radiographic evaluation of knee osteoarthritis. Aims: This study aimed to evaluate the quadriceps strength and bone properties of the calcaneus for each KL grade, and to clarify the impact of knee osteoarthritis grade on quadriceps strength and bone properties. Methods: This prospective cross-sectional study included data on 108 male patients (213 knees), aged ≥80 years, who could walk independently. A handheld dynamometer was used to measure quadriceps strength. Bone properties were evaluated using broadband ultrasound attenuation with a portable bone densitometer. Weight-bearing standing knee radiographs were evaluated using KL classification. Quadriceps strength and bone properties were evaluated for each KL grade and the correlations between the grade and quadriceps strength and bone properties were assessed simultaneously. Results: The numbers of participants in KL grades I–IV were 46, 102, 45, and 20, respectively. There were no differences among grades for either quadriceps strength or bone properties. Conclusions: Participants exhibited good quadriceps strength and bone properties regardless of their KL grade. Relatively high mechanical loading of muscle and bone incurred while walking independently, likely explaining this result. Clinically, this study demonstrated the absence of correlations between KL grade and quadriceps strength and bone properties, as was previously reported in studies showing the absence of a correlation between KL grade and pain.
Book chapters on the topic "Kellgren- Lawrence classification grades"
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Thongpat, Pongphak, Napat Pongsakonpruttikul, and Chayanin Angthong. "Detection of Knee Osteoarthritis using Artificial Intelligence." In Osteoporosis, Osteoarthritis and Rheumatoid Arthritis: An Agonizing Skeletal Triad. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815196085123010005.
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Knee osteoarthritis (KOA) is a common degenerative joint disease that results in disability due to joint dysfunction and pain. Almost one-fifth of early KOA cases are missed during the routine practice resulting in the progression of the disease. This narrative review aimed to explore and analyze various literatures that proposed Convoluted Neural Network (CNN) model in detecting KOA and its severity based on Kellgren Lawrence grading classification. At first, 221 publications were retrieved using the search term “artificial intelligence” and Knee osteoarthritis”. Only studies that used CNN and radiographic images were included in this study in which only 14 studies fitted our inclusion criteria. Each paper was thoroughly investigated for the input data and CNN model adopted as well as the performance and limitation of that study. Lastly, the conclusion was made and discussed using these results. Object detection and Classification models were among the most popular techniques adopted. Our results showed that object detection models were overall superior regarding the accuracy in the detection of KOA and its severity. The application of CNN for the detection of KOA from radiographic images has shown great promise where each technique has its own advantage. In the foreseeable future, the combination of object detection and classification detection may provide excellent potential as a merit tool to help orthopedists and related physicians for the proper diagnosis and treatment of KOA.