Classification Of Knee Osteoarthritis Research Articles

KOC_Net: Impact of the Synthetic Minority Over-Sampling Technique with Deep Learning Models for Classification of Knee Osteoarthritis Using Kellgren–Lawrence X-Ray Grade

One of the most common diseases afflicting humans is knee osteoarthritis (KOA). KOA occurs when the knee joint cartilage breaks down, and knee bones start rubbing together. The diagnosis of KOA is a lengthy process, and missed diagnosis can have serious consequences. Therefore, the diagnosis of KOA at an initial stage is crucial which prevents the patients from Severe complications. KOA identification using deep learning (DL) algorithms has gained popularity during the past few years. By applying knee X-ray images and the Kellgren–Lawrence (KL) grading system, the objective of this study was to develop a DL model for detecting KOA. This study proposes a novel model based on CNN called knee osteoarthritis classification network (KOC_Net). The KOC_Net model contains 05 convolutional blocks, and each convolutional block has three components such as Convlotuioanl2D, ReLU, and MaxPooling 2D. The KOC_Net model is evaluated on two publicly available benchmark datasets which consist of X-ray images of KOA based on the KL grading system. Additionally, we applied contrast-limited adaptive histogram equalization (CLAHE) methods to enhance the contrast of the images and utilized SMOTE Tomek to deal with the problem of minority classes. For the diagnosis of KOA, the classification performance of the proposed KOC_Net model is compared with baseline deep networks, namely Dense Net-169, Vgg-19, Xception, and Inception-V3. The proposed KOC_Net was able to classify KOA into 5 distinct groups (including Moderate, Minimal, Severe, Doubtful, and Healthy), with an AUC of 96.71%, accuracy of 96.51%, recall of 91.95%, precision of 90.25%, and F1-Score of 96.70%. Dense Net-169, Vgg-19, Xception, and Inception-V3 have relative accuracy rates of 84.97%, 81.08%, 87.06%, and 83.62%. As demonstrated by the results, the KOC_Net model provides great assistance to orthopedics in making diagnoses of KOA.

A Review for automated classification of knee osteoarthritis using KL grading scheme for X-rays

A Review for automated classification of knee osteoarthritis using KL grading scheme for X-rays

Accurate, automated classification of radiographic knee osteoarthritis severity using a novel method of deep learning: Plug-in modules

BackgroundFine-grained classification deals with data with a large degree of similarity, such as cat or bird species, and similarly, knee osteoarthritis severity classification [Kellgren–Lawrence (KL) grading] is one such fine-grained classification task. Recently, a plug-in module (PIM) that can be integrated into convolutional neural-network-based or transformer-based networks has been shown to provide strong discriminative regions for fine-grained classification, with results that outperformed the previous deep learning models. PIM utilizes each pixel of an image as an independent feature and can subsequently better classify images with minor differences. It was hypothesized that, as a fine-grained classification task, knee osteoarthritis severity may be classified well using PIMs. The aim of the study was to develop this automated knee osteoarthritis classification model.MethodsA deep learning model that classifies knee osteoarthritis severity of a radiograph was developed utilizing PIMs. A retrospective analysis on prospectively collected data was performed. The model was trained and developed using the Osteoarthritis Initiative dataset and was subsequently tested on an independent dataset, the Multicenter Osteoarthritis Study (test set size: 17,040). The final deep learning model was designed through an ensemble of four different PIMs.ResultsThe accuracy of the model was 84%, 43%, 70%, 81%, and 96% for KL grade 0, 1, 2, 3, and 4, respectively, with an overall accuracy of 75.7%.ConclusionsThe ensemble of PIMs could classify knee osteoarthritis severity using simple radiographs with a fine accuracy. Although improvements will be needed in the future, the model has been proven to have the potential to be clinically useful.

Classification and risk estimation of osteoarthritis using deep learning methods

The classification of knee osteoarthritis is solely based on contextual factors, with image processing algorithms playing a significant role in computer-aided diagnosis (CAD) systems. The inconsistent real-time pre-processing, on the other hand, has a significant impact on the diagnosing process. In this work, a Densely Connected Fully Convolutional Network (DFCN) for knee osteoarthritis classifier based on multiple learning (ML) strategies effectively classify knee osteoarthritis on the basis of risk estimation. Spatial osteoarthritis contextual vectors extracted by identifying the relationship between contextual variables using a machine learning approach. The hidden convolutional layers are used to compute edge interpretation, contextual cues, and input correction. The fused layer, which is simply a concentration of derived features, supports automatic learning of contextual features of osteoarthritis classification. The standard datasets from the Osteoarthritis Initiative (OAI) and the Multicentre Osteoarthritis Study (MOST) are used for experimental purposes to validate the proposed method. The results shows that the proposed DFCN is significantly improves the feature recognition for accurate classification around 94 % which is significantly higher than existing CNN results and flexibility to real-time implementation in the CAD system. It can also be used to automatically detect osteoarthritis types using a lightweight CNN architecture.

Corrigendum to: Performance analysis of Alexnet for Classification of Knee Osteoarthritis.

In the online version of the article, a change was made in the section of author's affiliation. The affiliation of Drs. Sivakumari and Vani in the online version of the article entitled "Performance analysis of Alexnet for Classification of Knee Osteoarthritis has been updated in "Current Medical Imaging", 2024; 20: e090823219574 [1]. The original article can be found online at: https://www.eurekaselect.com/article/133483 Original: T. Sivakumari1 and R. Vani1* 1SRM Institute of Science and Technology, Faculty of Engineering and Technology, Ramapuram, Chennai, Tamil Nadu, India Corrected: T. Sivakumari1 and R. Vani1* 1Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Faculty of Engineering and Technology, Ramapuram, Chennai, Tamil Nadu, India.

Automated system for classifying uni-bicompartmental knee osteoarthritis by using redefined residual learning with convolutional neural network

Knee Osteoarthritis (OA) is one of the most common joint diseases that may cause physical disability associated with a significant personal and socioeconomic burden. X-ray imaging is the cheapest and most common method to detect Knee (OA). Accurate classification of knee OA can help physicians manage treatment efficiently and slow knee OA progression. This study aims to classify knee OA X-ray images according to anatomical types, such as uni or bicompartmental. The study proposes a deep learning model for classifying uni or bicompartmental knee OA based on redefined residual learning with CNN. The proposed model was trained, validated, and tested on a dataset containing 733 knee X-ray images (331 normal Knee images, 205 unicompartmental, and 197 bicompartmental knee images). The results show 61.81 % and 68.33 % for accuracy and specificity, respectively. Then, the performance of the proposed model was compared with different pre-trained CNNs. The proposed model achieved better results than all pre-trained CNNs.

A Comparative Study of Machine Learning Classifiers for Enhancing Knee Osteoarthritis Diagnosis

Knee osteoarthritis (KOA) is a leading cause of disability, particularly affecting older adults due to the deterioration of articular cartilage within the knee joint. This condition is characterized by pain, stiffness, and impaired movement, posing a significant challenge in medical diagnostics and treatment planning, especially due to the current inability for early and accurate detection or monitoring of disease progression. This research introduces a multifaceted approach employing feature extraction and machine learning (ML) to improve the accuracy of diagnosing and classifying KOA stages from radiographic images. Utilizing a dataset of 3154 knee X-ray images, this study implemented feature extraction methods such as Histogram of Oriented Gradients (HOG) with Linear Discriminant Analysis (LDA) and Min–Max scaling to prepare the data for classification. The study evaluates six ML classifiers—K Nearest Neighbors classifier, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree, Random Forest, and XGBoost—optimized via GridSearchCV for hyperparameter tuning within a 10-fold Stratified K-Fold cross-validation framework. An ensemble model has also been made for the already high-accuracy models to explore the possibility of enhancing the accuracy and reducing the risk of overfitting. The XGBoost classifier and the ensemble model emerged as the most efficient for multiclass classification, with an accuracy of 98.90%, distinguishing between healthy and unhealthy knees. These results underscore the potential of integrating advanced ML methodologies for the nuanced and accurate diagnosis and classification of KOA, offering new avenues for clinical application and future research in medical imaging diagnostics.

Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal.

To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis. This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously. The CNN presented an area under the receiver operating characteristic curve of 0.866 (95% CI: 0.842-0.882). The model can be optimized to achieve, not simultaneously, maximum values of 0.907 for accuracy, 0.938 for sensitivity, and 0.994 for specificity. The proposed CNN can be used as a screening tool, reducing the total number of examinations evaluated by the radiologists of the study, and as a double-reading tool, contributing to the reduction of possible interpretation errors.

Classification of knee osteoarthritis based on quantum-to-classical transfer learning

Quantum machine learning takes advantage of features such as quantum computing superposition and entanglement to enable better performance of machine learning models. In this paper, we first propose an improved hybrid quantum convolutional neural network (HQCNN) model. The HQCNN model was used to pre-train brain tumor dataset (MRI) images. Next, the quantum classical transfer learning (QCTL) approach is used to fine-tune and extract features based on pre-trained weights. A hybrid quantum convolutional network structure was used to test the osteoarthritis of the knee dataset (OAI) and to quantitatively evaluate standard metrics to verify the robustness of the classifier. The final experimental results show that the QCTL method can effectively classify knee osteoarthritis with a classification accuracy of 98.36%. The quantum-to-classical transfer learning method improves classification accuracy by 1.08%. How to use different coding techniques in HQCNN models applied to medical image analysis is also a future research direction.

Correlation analysis between imaging classification of varus knee osteoarthritis and axis angle of tibiofemoral and patellofemoral joints

To explore correlation between imaging classification of knee osteoarthritis (KOA) and axis angle of tibiofemoral and patellofemoral joints. A retrospective analysis of 739 middle-aged and elderly patients with KOA (1 026 knee joints) who underwent vertical X-ray examination of both lower limbs and lateral knee joints from September 2018 to December 2020. Among them, 63 patients with K-L 0 grade (95 knee joints), 100 patients with K-L 1 grade (130 knee joints), 161 patients with K-L 2 grade (226 knee joints), 187 patients with K-L 3 grade (256 knee joints), and 228 patients of K-L 4 grade (319 knee joints). According to relative position of knee joint center and line between hip joint center and ankle joint center, the affected knee was divided into varus group(844 knees joints) and valgus group (182 knees joints). According to Install-Salvati method, the affected knee was divided into three groups, such as high patella (patella height>1.2 mm, 347 knees joints), median patella (patella height ranged from 0.8 to 1.2 mm, 561 knees joints), and low patella (patella height<0.8 mm, 118 knees joints). Lower femur angle, upper tibia angle, femoral neck shaft angle, femoral tibial angle, joint gap angle, hip-knee-ankle angle, patella-femoral angle and patella height among different groups were observed and compared. (1) In varus KOA group, there were statistical differnces in hip-knee-ankle angle, tibiofemoral angle, lower femoral angle, upper tibial angle, joint space angle, and femoral neck shaft angle of patients with different K-L grades (P<0.05). Hip-knee-ankle angle, tibiofemoral angle, lower femoral angle, upper tibial angle, joint space angle and K-L grade were significantly positively correlated at 0.01(P<0.05);femoral neck shaft angle and K-L grade showed negative correlation at 0.01(P<0.05). (2) In valgus KOA group, hip-knee-ankle angle, there were statistical differences in tibiofemoral angle, inferior femoral angle, superior tibial angle, joint space angle, and femoral neck shaft angle of patients with different K-L grades(P<0.05). Hip-knee-ankle angle, tibiofemoral angle, lower femoral angle, upper tibial angle, and femoral neck shaft angle showed negative correlation with K-L grades at level of 0.01 (P<0.05);joint gap angle and K-L grades showed significantly positive correlation at level of 0.01(P<0.05). (3) In high patella group, there were statistically differences in patellar height and patellar femoral angle of different K-L grades(P<0.05);there were no statistical difference in patella height and patellar femoral angle of different K-L grades in median patella group. There was no significant difference in patella heightin low patella group with different K-L grades(P>0.05), and there was statistical difference in patellofemoral angle(P<0.05). Patellar height and patella-femoral angle of high patella group were significantly positively correlated with K-L grades at the level of 0.01 (P<0.05);patella height and patella-femoral angle were not correlated with K-L grades in median patella group(P>0.05). There was no correlation between height of patella and K-L grade in low patella group (P>0.05). There was significant negative correlation between patella-femoral angle and K-L grade at level of 0.05 (P<0.05). Inferior femoral angle, tibiofemoral angle, joint gap angle, hip-knee-ankle angle, femoral neck shaft angle and high patella are related to K-L classification of varus KOA, which could be used for early diagnosis and provide objective data for efficacy analysis of conservative treatment.

Disentanglement of Latent Factors of Radiographic Knee Images for Knee Osteoarthritis Severity Classification

Knee osteoarthritis is the most common form of the musculoskeletal disorder that usually happens to the elderly. The diagnosis and treatment for knee osteoarthritis are causing a huge economic burden to society. Traditionally, the diagnosis for knee osteoarthritis was done by analyzing MRI (Magnetic Resonance Imaging). However, this method has problems in the way that it is costly and has limited access since they are only available in specialized medical institutions. There have been many researches that attempt to use x-ray images that are safe, cost-efficient, and commonly available. Their method often fails and shows poor results due to a lack of training dataset. In this paper, I proposed a novel autoencoder-based knee osteoarthritis classification system. Unlike the previous deep learning-based research, the proposed method disentangles the osteoarthritis-related latent factors from knee x-ray images. These latent factors are then trained to predict the severity of osteoarthritis. The proposed method achieves accuracy of 78.9% on the publicly available dataset. Our method produces accurate results without having a large dataset while successfully avoiding the curse of dimensionality. Throughout the comprehensive experiment, I have shown that the proposed method outperforms the existing state-of-the-art methods by a great accuracy margin.

Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models.

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

Knee osteoarthritis pre-screening questionnaire (KOPS): cross-cultural adaptation and measurement properties of the brazilian version—KOPS Brazilian version

BackgroundDespite the criteria already established for the classification of knee osteoarthritis (OA), a radiographic and/or clinical knee OA diagnosis usually occurs in cases of fully manifest or more advanced disease, which can make health promotion, prevention, and functional rehabilitation in more advanced stages of the disease less effective. In addition, radiographic knee OA can generate more financial costs for health services. Therefore, developing and validating screening instruments to assess the probability of development and progression of knee OA would be of great value for both clinical practice and science.ObjectiveTo cross-culturally adapt and investigate the measurement properties of the Knee OA Pre-screening Questionnaire Brazilian version.MethodsA total of 250 individuals of both sexes aged between 35 and 92 years [(mean (standard deviation): 63 (11) years old; 74.1 (15.1) kg; 1.59 (0.09) m; 29.38 (5.44) kg/m2] participated in this study. The cross-cultural adaptation and analyses of the measurement properties of the KOPS Brazilian version included: (1) assessment of conceptual and item equivalence; (2) assessment of semantic equivalence; (3) assessment of operational equivalence; and (4) assessment of measurement equivalence, reliability, and validity.ResultsCronbach’s alpha for the internal consistency among the six components of the KOPS Brazilian version was 0.71. The test-retest 72 h apart for each component resulted in a coefficient correlation intraclass ranging from 0.74 to 1.00. The probability of an individual randomly chosen from the population having KL ≥ 1 and KOPS Brazilian version ≥ 21 points was 0.74 (area under the curve of the Receiver Operating Characteristic – AUC of ROC); furthermore, the AUC for KL ≥ 2 and the KOPS Brazilian version ≥ 23 points was 0.77.ConclusionThe KOPS Brazilian version is a reliable and valid instrument for early screening of knee OA in individuals aged 35 years and over in the Brazilian context.

Determining the Efficacy and Safety of Duloxetine and Gabapentin in Pain Reduction on Knee Osteoarthritis Patients – An Observational Study

Background&#x0D; Prevalence of knee osteoarthritis is rising in India probably due to the larger proportion of the population attaining longevity and obesity becoming very common in the population. This has subsequently led to increasing demand for knee arthroplasty. Correct diagnosis and classification of knee osteoarthritis (OA) has therefore provoked greater interest in following up and planning for either conservative or operative management. &#x0D; Method&#x0D; We selected 120 radiographic examinations of the knee taken of patients visiting the SriMedilife Rheumatology Clinic at Warangal and manually categorized them on the basis of the Kellgren and Lawrence grading. &#x0D; Result&#x0D; In the present study, it has been observed that “both Gabapentin and Duloxetine had similar efficacy that was acceptable at the end of the three months in the treatment for reducing pain and improving WOMAC score and its subscales with no significant difference between the groups (p=0.3889). However, the time required for Duloxetine to produce significant effects was after first month, while the effects of Gabapentin gradually began to be observed only after the second month of its treatment.”&#x0D; Conclusion&#x0D; We conclude that both gabapentin and duloxetine have approximately similar and acceptable effects in pain reduction and improvement of functional status in patients with knee OA at the end of the third month’s treatment.

Is interleukin-17 implicated in early knee osteoarthritis pathogenesis as in rheumatoid arthritis?

BackgroundInterleukin-17 (IL-17) is a cytokine that promotes activation of multiple catabolic pathways resulting in cartilage and tissue damage. It has features making it increasingly attractive as a biological marker, especially in rheumatoid arthritis (RA) and osteoarthritis (OA). However, its expression is heterogeneous; not all patients’ exhibit high IL-17 levels, and its level along the disease course is still challenging to predict.Aim of the workThe objectives of this study were to compare serum IL-17 levels in patients with early knee OA and in RA patients, to determine its correlation with disease activity in RA and to determine if it is correlated with functional scores in both RA and OA.Subjects and methodsTwenty early knee OA patients (32.7 ± 3.7) years were included. Diagnosis of early OA was based on Luyten et al. 2012 early knee OA classification (early OA 2012). This study also included 25 RA patients aged 32.8 ± 5.1 years, and the diagnosis was according to 2010 ACR-EULAR classification criteria for RA. The current work also included a control group of 20 healthy volunteers aged 31.9 ± 3.2 years. The serum IL-17 level was assessed by using the ELISA technique.ResultsSerum IL-17 level was significantly high in early knee OA patients (5.2 pg/ml) and was significantly higher in RA patients (5.9 pg/ml) compared to the control group (4.9 pg/ml) (P < 0.001).ConclusionsThe increased serum IL-17 level in patients with early knee OA suggests its pathogenic role in the disease. Serum IL-17 positive correlation with the severity of knee OA-related pain proposes that it may be a potential marker to target for early treatment of knee OA-related pain.

THE CONSTRUCT VALIDITY OF DIFFERENT KNEE OSTEOARTHRITIS CLASSIFICATION CRITERIA IN THE ELSA-BRASIL MUSCULOSKELETAL STUDY

Purpose: The existence of different knee osteoarthritis (OA) classification criteria makes it difficult to investigate risk factors and clinical manifestations associated with knee OA (KOA). We evaluated the construct validity of different knee OA classification criteria by assessing the association between these criteria and body mass index (BMI), current pain, and subjective and objective functionality.

A ResNet‐based approach for accurate radiographic diagnosis of knee osteoarthritis

Currently X-ray images are clinically graded by experienced clinicians using the Kellgren and Lawrence (KL) scoring method. However, individual scoring is subjective and error prone. This study proposes an approach for automated knee osteoarthritis classification based on deep neural networks. The knee X-ray images are first pre-processed with frequency-domain filtering and histogram normalisation, making the trabecular bone texture more obvious and benefiting the subsequent classification task. Then, a two-step classification strategy is proposed by extracting the joint centre based on the VGG network and classifying osteoarthritis grades based on the ResNet-50 network. In addition, a rebalance operation is proposed to deal with the dataset unbalance problem, and a quick search technique is proposed to improve the iterative search efficiency for the joint centre. With all of these techniques, a classification accuracy of 81.41% is obtained, which is higher compared to the state-of-the-art approaches.

Increase in tibial internal rotation due to weight-bearing is a key feature to diagnose early-stage knee osteoarthritis: a study with upright computed tomography

BackgroundThe classification of knee osteoarthritis is an essential clinical issue, particularly in terms of diagnosing early knee osteoarthritis. However, the evaluation of three-dimensional limb alignment on two-dimensional radiographs is limited. This study evaluated the three-dimensional changes induced by weight-bearing in the alignments of lower limbs at various stages of knee osteoarthritis.MethodsForty five knees of 25 patients (69.9 ± 8.9 years) with knee OA were examined in the study. CT images of the entire leg were obtained in the supine and standing positions using conventional CT and 320-row detector upright CT, respectively. Next, the differences in the three-dimensional alignment of the entire leg in the supine and standing positions were obtained using 3D-3D surface registration technique, and those were compared for each Kellgren–Lawrence grade.ResultsGreater flexion, adduction, and tibial internal rotation were observed in the standing position, as opposed to the supine position. Kellgren–Lawrence grades 1 and 4 showed significant differences in flexion, adduction, and tibial internal rotation between two postures. Grades 2 and 4 showed significant differences in adduction, while grades 1 and 2, and 1 and 3 showed significant differences in tibial internal rotation between standing and supine positions.ConclusionsWeight-bearing makes greater the three-dimensional deformities in knees with osteoarthritis. Particularly, greater tibial internal rotation was observed in patients with grades 2 and 3 compared to those with grade 1. The greater tibial internal rotation due to weight-bearing is a key pathologic feature to detect early osteoarthritic change in knees undergoing osteoarthritis.

Automatic detection and classification of knee osteoarthritis using deep learning approach.

We developed a tool for locating and grading knee osteoarthritis (OA) from digital X-ray images and illustrate the possibility of deep learning techniques to predict knee OA as per the Kellgren-Lawrence (KL) grading system. The purpose of the project is to see how effectively an artificial intelligence (AI)-based deep learning approach can locate and diagnose the severity of knee OA in digital X-ray images. Selection criteria: Patients above 50years old withOAsymptoms (knee joint pain, stiffness, crepitus, and functional limitations) were included in the study. Medical experts excluded patients with post-surgical evaluation, trauma, and infection from the study. We used 3172Anterior-posterior view knee joint digital X-ray images. We have trained the FasterRCNNarchitecture to locate the knee joint space width (JSW) region in digital X-ray images and we incorporate ResNet-50 with transfer learning to extract the features. We have used another pre-trained network (AlexNet with transfer learning) for the classification of knee OA severity. We trained the region proposal network (RPN) using manual extract knee area as the ground truth image and the medical experts graded the knee joint digital X-ray images based on the Kellgren-Lawrence score. An X-ray image is an input for the final model, and the output is a Kellgren-Lawrence grading value. The proposed model identified the minimal kneeJSWarea with a maximum accuracy of 98.516%, and the overall kneeOAseverity classification accuracy was 98.90%. Today numerous diagnostic methods are available, but tools are not transparent and automated analysis ofOAremains a problem. The performance of the proposed model increases while fine-tuning the network and it is higher than the existing works. We will extend this work to grade OA in MRI data in the future.

Artificial intelligence assistance in radiographic detection and classification of knee osteoarthritis and its severity: a cross-sectional diagnostic study.

Radiographic interpretation suffers from an ever-increasing workload in orthopedic and radiology departments. The present study applied and assessed the performance of a convolutional neural network designed to assist orthopedists and radiologists in the detection and classification of knee osteoarthritis from early to severe degrees in accordance with the Kellgren-Lawrence (KL) classification system. In total, 1650 knee joint radiographs (anteroposterior view) were collected from the Osteoarthritis Initiative public resource. Two models were developed: one distinguished normal (KL 0-I) from osteoarthritic knees (KL II-IV), and the other classified the severity as normal (KL 0-I), non-severe (KL II), or severe (KL III-IV). The regions of interest were labeled under the supervision of experts. Our artificial intelligence (AI) models were trained using the You Only Look Once version 3 (YOLOv3) detection algorithm. Our first AI model using YOLOv3 tiny could detect and classify normal and osteoarthritic knees on plain knee joint radiographs with 85% accuracy and 81% mean average precision. The second AI model for classifying severity achieved a total accuracy of 86.7% and mean average precision of 70.6%. Our proposed deep learning models provided high accuracy and satisfactory precision for the detection and classification of early to severe knee osteoarthritis on anteroposterior radiographs. These models may be used as diagnostic aids by interpreting knee radiographs and guiding the treatment options via each osteoarthritic stage for related physicians and specialists.