Knee Osteoarthritis Grade Research Articles

Grading of Knee Osteoarthritis Based on Kellgren-Lawrence Classification and Finding an Association Between Radiographic Features and Pain: A Cross-Sectional Study at a Tertiary Health Care Hospital

Grading of Knee Osteoarthritis Based on Kellgren-Lawrence Classification and Finding an Association Between Radiographic Features and Pain: A Cross-Sectional Study at a Tertiary Health Care Hospital

A biopsychosocial approach to phenotyping people with knee osteoarthritis awaiting total knee arthroplasty: A secondary cohort analysis

BackgroundPrevious research showed chronic post-total knee arthroplasty (TKA) pain in 20% of people with knee osteoarthritis (KOA). Various preoperative biopsychosocial-related factors have been described, but phenotyping people with KOA awaiting TKA based on these factors is still lacking. This could be relevant to understanding differences in TKA surgery responses. ObjectiveTo identify phenotypes in people with KOA awaiting TKA and differences in post-TKA pain based on preoperative biopsychosocial factors. MethodsPeople with KOA awaiting TKA in 4 hospitals in Belgium and the Netherlands were included. A cross-sectional latent profile analysis was conducted on structural, metabolic, functional, pain-related, psychological and social variables. Concurrent validity was tested using 3-step multinomial logistic regression. The difference in one-year post-TKA pain was examined with linear mixed model analysis. ResultsTwo hundred and seventeen participants were included in the latent profile analysis with a mean (SD) age of 65.5 (7.7) years, including 109 women. A model with 2 phenotypes differed in 14 out of 21 variables. Participants with phenotype 2 (28%) had a higher body mass index (BMI), higher chance of having less structural damage (KOA grade), lower mean quadriceps strength and physical function (Knee Society Scoring System functional and 30-second chair stand test), higher pain intensity, number of pain locations, and indices of central sensitisation (temporal summation, central sensitisation inventory score, and lower pressure pain thresholds), higher pain catastrophising, anxiety and depression, and higher post-TKA pain intensity compared to phenotype 1 (72%). Concurrent validity was confirmed in 3 out of 4 variables. ConclusionsPhenotype 2 (28%) with nociplastic pain characteristics in combination with worse psychological factors, BMI, functional and structural factors, and phenotype 1 (72%) not representing these characteristics were identified. Phenotype 2 had worse pain intensity scores after TKA compared to phenotype 1. Attention to the characteristics of phenotype 2 is warranted concerning post-TKA pain. Database registrationThe protocol is registered at ClinicalTrials.gov (NCT05380648).

Establishment of rabbit knee osteoarthritis model by simple anterior cruciate ligament disruption

The rabbit knee osteoarthritis(KOA) model was established by simple anterior cruciate ligament disruption. The selected 40 adult white rabbits were randomly divided into two groups, blank group with 6 rabbits and model group with 34 rabbits. The rabbits in model group were given the disruption of anterior cruciate ligament of right knee joint. In the fourth, eighth and the twelfth weeks of modeling, different degrees of degenerative changes have been observed on the rabbits articular cartilages from both macroscopic and microscopic aspects. At 4, 8 and 12 weeks after modeling, the articular cartilage of the rabbits in the model group showed degenerative changes in different degrees. At 8 and 12 weeks, the typical pathological manifestations of KOA in the middle and late stages were found in the infrapatellar fat pad and synovium, respectively. There were no changes in articular cartilage, infrapatellar fat pad and synovial membrane in the blank group at the same time. There were significant differences in the gross score between the model group and the blank group at 4 weeks [(1.300±0.674) vs 0.000], 8 weeks [(2.400±0.516) vs 0.000] and 12 weeks [(3.583±0.668) vs 0.000] (P<0.05), the histopathological scores were significantly different, at 4 weeks [(2.900±0.567) vs 0.000], 8 weeks [(7.500±1.178) vs (0.500±0.707)], 12 weeks[(11.833±1.337) vs (1.500±0.707)], (P<0.05). The establishment of rabbit knee arthritis model by simply cutting the anterior cruciate ligament is simple and feasible, with good stability, high success rate, small trauma, short modeling cycle and less changes in animal physiological structure. The rabbit knee arthritis model established by simply cutting the anterior cruciate ligament has good modeling effect on different stages and grades of knee osteoarthritis, and the postoperative infection and trauma can be controlled, which can meet the test requirements.

Deep Learning for Automatic Knee Osteoarthritis Severity Grading and Classification.

Knee osteoarthritis (OA) is a prevalent condition that significantly impacts the quality of life, often leading to the need for knee replacement surgery. Accurate and timely identification of knee degeneration is crucial for effective treatment and management. Traditional methods of diagnosing OA rely heavily on radiological assessments, which can be time-consuming and subjective. This study aims to address these challenges by developing a deep learning-based method to predict the likelihood of knee replacement and the Kellgren-Lawrence (KL) grade of knee OA from X-ray images. We employed the Osteoarthritis Initiative (OAI) dataset and utilized a transfer learning approach with the Inception V3 architecture to enhance the accuracy of OA detection. Our approach involved training 14 different models-Xception, VGG16, VGG19, ResNet50, ResNet101, ResNet152, ResNet50V2, ResNet101V2, ResNet152V2, Inception V3, Inception, ResNetV2, DenseNet121, DenseNet169, DenseNet201-and comparing their performance. The study incorporated pixel ratio computation and picture pre-processing, alongside a decision tree model for prediction. Our experiments revealed that the Inception V3 model achieved the highest training accuracy of 91% and testing accuracy of 67%, with notable performance in both training and validation phases. This model effectively identified the presence and severity of OA, correlating with the Kellgren-Lawrence scale and facilitating the assessment of knee replacement needs. By integrating advanced deep learning techniques with radiological diagnostics, our methodology supports radiologists in making more accurate and prompt decisions regarding knee degeneration. The Inception V3 model stands out as the optimal choice for knee X-ray analysis, contributing to more efficient and timely healthcare delivery for patients with knee osteoarthritis.

The Role of Serum Level of Col2a1 and Hyaluoronic Acid in Diagnosis of Knee Osteoarthritis

Osteoarthritis (OA) is a complex condition in which affected tissues experience changes in metabolism, structure, biochemistry, and function. This condition arises when chondrocytes fail to balance the synthesis and degradation of extracellular components like proteoglycans and collagens, leading to inflammation of the synovial membrane and joint capsule. Aim of the study: This study was designed to evaluate the serum levels of Type II Collagen alpha 1 (coll2a1) and Hyaluronic Acid (HA) in diagnosis of early stage of knee osteoarthritis in Iraqi patients Subjects and Methods: This case- control study was carried out at Department of Biochemistry, College of Medicine, University of Baghdad and at Rheumatology Department , Baghdad Hospital / Medical City, during the period from September 2022 to March 2023. It included ninety volunteer males and females subdivided into subgroups: Group 1consisted of 50 patients who have had early stages of knee osteoarthritis (KOA) and group 2 involved 40 apparently healthy subjects who served as control group. The diagnosis of KOA was made by a consultant rheumatologist using American College of Rheumatology (ACR) criteria. Moreover, patients were sub- classified according to their BMI (normal weight, overweight and obese) and KOA severity (grad1 and grad 2). Investigations included serum measurements of Human serum coll2a1( type II collagen alpha I) and Human serum HA (Hyaluronic Acid) in all patients and controls by ELISA technique. Results: The mean (±SD) values of Coll2a1, HA, of KOA patients were significantly higher than those of controls (for all, p &lt;0.01). There was significant positive correlation between. The mean value of serum hyaluronic acid was significantly increased in grad 2 KOA when compared with grad 1 KOA patients (p &lt; 0.004). The results found that fibulin-3 has the highest receiver operator characteristic (ROC) and area under curve (AUC=89.2% ) at cutoff (7.50 ng/ml) with sensitivity 80.0 and specificity 80.0 in diagnosis of KOA. Also, serum Coll2a1 has high diagnostic utility (AUC=88.6%) at cutoff (286.00 pg/ml) with sensitivity 82.0 and specificity 80.0 . Conclusion: Assessing non-invasive serum levels of coll2a1 (with a cutoff of 286.00 pg/ml) offers a new biochemical marker for diagnosing the early stages of knee osteoarthritis (KOA) potentially before changes become visible on X-rays. Additionally, measuring serum hyaluronic acid levels could serve as an indicator of disease severity, helping to differentiate between various grades of KOA.

Time Course Effects of Kinesio Taping in Knee Osteoarthritis Patients

Background: Knee osteoarthritis is a common musculoskeletal disorder in the elderly, characterized by cartilage loss leading to pain, stiffness, and restricted movement. Kinesiotaping has been suggested as a therapeutic approach to reduce pain and improve functional outcomes in knee osteoarthritis patients. Objective: This study aimed to compare the effects of quadriceps and patellar kinesiotaping applied for different durations (24, 48, and 72 hours) on pain, balance, and functional performance in patients with knee osteoarthritis. Methods: A randomized clinical trial was conducted from March to September 2020 at the Railway General Hospital, Rawalpindi, and Abasyn Rehabilitation Center, Islamabad. Forty-eight patients with knee osteoarthritis (grades 2-3) were randomly assigned to three groups: Group A (kinesiotaping for 24 hours), Group B (kinesiotaping for 48 hours), and Group C (kinesiotaping for 72 hours). All participants also received conventional therapy, including hot pack application, quadriceps strengthening exercises (15 repetitions × 3 sets), and stretching exercises (5 repetitions with a 10-second hold × 3 sets). Pain, balance, and functional performance were assessed at baseline, post-intervention, and one week later using the Numeric Pain Rating Scale (NPRS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Functional Reach Test (FRT), and Time Up and Go Test (TUG). Data were analyzed using SPSS version 25, with intragroup comparisons conducted using the Wilcoxon signed-rank test and intergroup comparisons using the Kruskal-Wallis test. A p-value of less than 0.05 was considered statistically significant. Results: Significant reductions in pain were observed across all groups (p &lt; 0.05), with the most notable improvement in Group C (72 hours), where NPRS scores decreased from a median of 2 (IQR 1.75) at baseline to 1 (IQR 1) at follow-up (p &lt; 0.05). However, no significant improvements were noted in balance or functional performance across the groups, as indicated by FRT, WOMAC, and TUG scores (p &gt; 0.05). Conclusion: Kinesiotaping significantly reduces pain in knee osteoarthritis patients, particularly when applied for 72 hours, but does not appear to significantly impact balance or functional performance. These findings suggest that kinesiotaping is effective for short-term pain management, though additional interventions may be necessary to improve functional outcomes.

Predictive Value of Neutrophil Lymphocyte Ratio in Assessing Severity Grades of Knee Osteoarthritis

Predictive Value of Neutrophil Lymphocyte Ratio in Assessing Severity Grades of Knee Osteoarthritis

AI and the Potential for Uniform and Scalable Grading of Knee Osteoarthritis.

AI and the Potential for Uniform and Scalable Grading of Knee Osteoarthritis.

Interobserver Agreement and Performance of Concurrent AI Assistance for Radiographic Evaluation of Knee Osteoarthritis.

Background Due to conflicting findings in the literature, there are concerns about a lack of objectivity in grading knee osteoarthritis (KOA) on radiographs. Purpose To examine how artificial intelligence (AI) assistance affects the performance and interobserver agreement of radiologists and orthopedists of various experience levels when evaluating KOA on radiographs according to the established Kellgren-Lawrence (KL) grading system. Materials and Methods In this retrospective observer performance study, consecutive standing knee radiographs from patients with suspected KOA were collected from three participating European centers between April 2019 and May 2022. Each center recruited four readers across radiology and orthopedic surgery at in-training and board-certified experience levels. KL grading (KL-0 = no KOA, KL-4 = severe KOA) on the frontal view was assessed by readers with and without assistance from a commercial AI tool. The majority vote of three musculoskeletal radiology consultants established the reference standard. The ordinal receiver operating characteristic method was used to estimate grading performance. Light kappa was used to estimate interrater agreement, and bootstrapped t statistics were used to compare groups. Results Seventy-five studies were included from each center, totaling 225 studies (mean patient age, 55 years ± 15 [SD]; 113 female patients). The KL grades were KL-0, 24.0% (n = 54); KL-1, 28.0% (n = 63); KL-2, 21.8% (n = 49); KL-3, 18.7% (n = 42); and KL-4, 7.6% (n = 17). Eleven readers completed their readings. Three of the six junior readers showed higher KL grading performance with versus without AI assistance (area under the receiver operating characteristic curve, 0.81 ± 0.017 [SEM] vs 0.88 ± 0.011 [P < .001]; 0.76 ± 0.018 vs 0.86 ± 0.013 [P < .001]; and 0.89 ± 0.011 vs 0.91 ± 0.009 [P = .008]). Interobserver agreement for KL grading among all readers was higher with versus without AI assistance (κ = 0.77 ± 0.018 [SEM] vs 0.85 ± 0.013; P < .001). Board-certified radiologists achieved almost perfect agreement for KL grading when assisted by AI (κ = 0.90 ± 0.01), which was higher than that achieved by the reference readers independently (κ = 0.84 ± 0.017; P = .01). Conclusion AI assistance increased junior readers' radiographic KOA grading performance and increased interobserver agreement for osteoarthritis grading across all readers and experience levels. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Assessment of Knee Osteoarthritis Severity using New Multifactorial Scale (KHIUS) in Northwest Syria

The present study aimed to identify the risk factors associated with knee osteoarthritis (OA) in Northwest Syria and to evaluate the reliability of our newly proposed Khatib-Khaled Idlib University Scale (KHIUS) in assessing knee OA severity. The study enrolled 101 patients with knee OA, diagnosed through X-ray at the orthopedic clinic. The Kellgren and Lawrence classification was employed to determine the X-ray knee OA grades. The erythrocyte sedimentation rate (ESR) value was obtained as a biomarker after excluding rheumatoid arthritis, other inflammatory diseases, and malignant tumors. The risk factors of knee OA assessed in our study included age, gender, BMI, and physical activity. Each patient completed the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire and KHIUS to evaluate knee OA severity. Correlation coefficients of two scales, i.e., WOMAC and X-ray knee OA grading as well as KHIUS and X-ray knee OA grading, were determined. The mean age of the patients was 52.84 ± 9.74 years (age range 25-80 years). Most patients had low daily activity levels, and the left knee was the most affected. In our study, the correlation coefficient between WOMAC and KHIUS was strong (R: 80.3%, P &lt; 0.01). The correlation coefficient between X-ray KL knee OA grades and WOMAC was moderate (R: 50.9%, P &lt; 0.01), whereas the correlation coefficient between X-ray KL knee OA grades and KHIUS was comparatively stronger (R: 75.7%, P &lt; 0.01). KHIUS can be a reliable scale to assess knee OA severity and to guide the method of treatment by orthopedic surgeons. In addition, KHIUS is more closely related to X-ray KL knee OA grading than other clinical scales.

Automatic grading of knee osteoarthritis with a plain radiograph radiomics model: combining anteroposterior and lateral images

ObjectivesTo establish a radiomics-based automatic grading model for knee osteoarthritis (OA) and evaluate the influence of different body positions on the model’s effectiveness.Materials and methodsPlain radiographs of a total of 473 pairs of knee joints from 473 patients (May 2020 to July 2021) were retrospectively analyzed. Each knee joint included anteroposterior (AP) and lateral (LAT) images which were randomly assigned to the training cohort and the testing cohort at a ratio of 7:3. First, an assessment of knee OA severity was done by two independent radiologists with Kallgren–Lawrence grading scale. Then, another two radiologists independently delineated the region of interest for radiomic feature extraction and selection. The radiomic classification features were dimensionally reduced and a machine model was conducted using logistic regression (LR). Finally, the classification efficiency of the model was evaluated using receiver operating characteristic curves and the area under the curve (AUC).ResultsThe AUC (macro/micro) of the model using a combination of AP and LAT (AP&LAT) images were 0.772/0.778, 0.818/0.799, and 0.864/0.879, respectively. The radiomic features from the combined images achieved better classification performance than the individual position image (p < 0.05). The overall accuracy of the radiomic model with AP&LAT images was 0.727 compared to 0.712 and 0.417 for radiologists with 4 years and 2 years of musculoskeletal diagnostic experience.ConclusionsA radiomic model constructed by combining the AP&LAT images of the knee joint can better grade knee OA and assist clinicians in accurate diagnosis and treatment.Critical relevance statementA radiomic model based on plain radiographs accurately grades knee OA severity. By utilizing the LR classifier and combining AP&LAT images, it improves accuracy and consistency in grading, aiding clinical decision-making, and treatment planning.Key PointsRadiomic model performed more accurately in K/L grading of knee OA than junior radiologists.Radiomic features from the combined images achieved better classification performance than the individual position image.A radiomic model can improve the grading of knee OA and assist in diagnosis and treatment.Graphical

Comparison of angiotensin converting enzyme 1 levels to the grading of knee osteoarthritis

Background and aim. One of the most prevalent chronic joint illnesses is osteoarthritis (OA), which can impair a patient's quality of life by causing joint pain, stiffness, deformity, and narrowed joint spaces. Many factors influence the pathogenesis of Osteoarthritis such as age and body weight, as well as genetic factors that are reported to play a role in the occurrence and progression of Osteoarthritis. Recently, many studies have demonstrated that inflammatory variables contribute to osteoarthritis pathomechanisms. It is currently thought that inflammatory variables alone are an independent risk factor for osteoarthritis. Methods. Observational sub-analysis with a cross-sectional approach to examine the relationship between Angiotensin Converting Enzyme 1 (ACE 1) levels and the grading of osteoarthritis at “Dr. Wahidin Sudirohusodo” Hospital from October 2020 to February 2021. ACE 1 levels were measured using the Elisa KIT and grading of osteoarthritis using the Kellgren and Lawrence criteria. Results. Analysis of the relationship between ACE 1 levels and osteoarthritis grading using the ANOVA test and Post Hoc Bonferonni Test showed a significant relationship between ACE 1 levels and osteoarthritis grading, where high ACE 1 levels were related to osteoarthritis grading. Deficiency and insufficiency (p&lt;0.05). The results of this study are in line with previous research. Conclusion. The higher the ACE 1 level, the more severe the degree of knee osteoarthritis.

Preoperative glycaemic control, number of pain locations, structural knee damage, self-reported central sensitisation, satisfactionand personal control are predictive of 1-year postoperative pain, and change in pain from pre- to 1-year posttotal knee arthroplasty.

The aim of this study was to identify preoperative predictors for 1-year posttotal knee arthroplasty (TKA) pain and pre- to post-TKA pain difference in knee osteoarthritis (KOA) patients. From March 2018 to July 2023, this prospective longitudinal cohort study enrolled KOA patients awaiting TKA from four hospitals in Belgium and the Netherlands. Different biopsychosocial predictors were assessed preoperatively by questionnaires and physical examinations (input variables). The Knee injury and Osteoarthritis Outcome Score (KOOS) subscale pain was used to measure pain intensity. The absolute KOOS subscale pain score 1-year post-TKA and the difference score (ΔKOOS = 1-year postoperative - preoperative) were used as primary outcome measures (output variables). Two multivariable linear regression analyses were performed. Two hundred and twenty-threeparticipants were included after multiple imputation. Worse absolute KOOS subscale pain scores 1-year post-TKA and negative or closer to zero ΔKOOS subscale pain scores were predicted by self-reported central sensitisation, lower KOA grade and preoperative satisfaction,and higher glycated haemoglobin, number of pain locationsand personal control (adjusted R2 = 0.25). Additional predictors of negative or closer to zero ΔKOOS subscale pain scores were being self-employed, higher preoperative pain and function (adjusted R2 = 0.37). This study reports different biopsychosocial predictors for both outcomes that have filtered out other potential predictors and providevalue for future studies on developing risk assessment tools for the prediction of chronic TKA pain. The protocol is registered at clinicaltrials.gov (NCT05380648) on 13 May 2022. Level II.

Extracorporeal Shock Wave Therapy as Regenerative Medicine in Knee Osteoarthritis: Pre-Experiment for Matrix Metalloproteinase-3 Evaluation.

Knee osteoarthritis (KOA) is currently treated by regenerative therapies that aim to inhibit arthritic degeneration. Extracorporeal shock wave therapy (ESWT) is one of the physical regenerative approaches used for KOA management. However, little is known regarding the impact of shock wave treatment on matrix metalloproteinase-3 (MMP-3), which is one of the enzymes mediating cartilage degradation. To evaluate the effect of ESWT on MMP-3 levels and pain intensity in patients with KOA. Fourteen patients diagnosed with Kellgren Lawrence, grades 2 and 3 KOA were recruited for the study. ESWT piezo shockwave was applied once a week for six weeks. MMP-3 levels in the blood were measured pre-test, mid-test (three weeks after therapy) and post-test (one week after the last session) by enzyme-linked immunosorbent assay (ELISA). The perceived pain was recorded at each session by the Wong Becker Face Scale. The median pre-test, mid-test and post-test MMP-3 levels were 19.92 ng/mL, 15.89 ng/mL and 18.82 ng/mL, respectively, and there were significant differences between the pre-test and mid-test, and the pre-test and post-test values (p < 0.05). The pain scores also decreased significantly over the period of intervention. MMP-3 levels decreased significantly in KOA patients after ESWT, and the decline was most obvious after 3 weeks of therapy. Therefore, EWST should be considered as a suitable treatment option for KOA.

Effect of kinesio taping and exercise on functional impairment in patients with different degrees of knee osteoarthritis

BACKGROUND: Kinesio Taping (KT) is used for musculoskeletal problems. KT optimizes and reduces mechanical stresses on soft tissues. However, the benefits of KT and traditional exercises for different severities of knee osteoarthritis (OA) remains controversial. OBJECTIVES: This study aimed to investigate the effects of tension KT and traditional physical therapy on different severities of knee OA. METHODS: Fifty-six male patients with knee OA, aged 60–75-y years, were allocated to groups A and B based on grades 1 and 2 and grades 3 and 4 on the Kellgren-Lawrence scale, respectively. Tension KT and traditional physical therapy were administrated to both groups for 6-wks. The extensor peak moment of quadriceps and knee flexion range of motion (ROM) were recorded using an isokinetic dynamometer and universal goniometer, respectively. Visual analog scale (VAS) was used to score pain level. Statistical analysis was performed using the ANCOVA test. RESULTS: ANCOVA revealed an increase in peak extensor moment of quadriceps (Pre: 51.6 ± 2.8 Nm; Post: 63.3 ± 4.3 Nm), knee flexion ROM (Pre: 112.8∘7.2; Post: 122.9∘4.8), and pain improvement (Pre: 5.1 ± 1.07; Post: 2.8 ± 1.6) in group A (p&lt; 0.001). Group B showed insignificant improvements in all outcome measures (p&gt; 0.05). CONCLUSION: KT with traditional physical therapy improved knee pain, quadriceps strength, and knee ROM in older males with knee OA grades 1 and 2. KT and traditional physical therapy were insufficient to produce considerable effects on grades 3 and 4 of knee OA.

Performance analysis of Alexnet for Classification of Knee Osteoarthritis.

In recent years, automated grading of knee osteoarthritis (KOA) has focused on determining disease progression. Clinical examinations and radiographic image review are necessary for diagnosis. Timely and accurate diagnosis, along with medical care, can slow down KOA progression. X-rays and MRI are crucial diagnostic tools. KOA diagnosis traditionally relies on radiologists' and clinicians' experience. However, the rapid development of deep learning technology (AI) offers promising solutions for medical applications. The objective of this study was to review and summarize various methods proposed by researchers for the automated grading of KOA. Additionally, this study aimed to evaluate the performance of the AlexNet model in classifying the severity of KOA. The performance of the AlexNet model has been compared to that of other models, and the results have been assessed. A comprehensive review of existing research on automated grading of KOA has been conducted. Various methods proposed by different researchers have been examined and summarized. The AlexNet model has been employed for classifying the severity of KOA, and its performance has been evaluated. A comparative analysis has been carried out to compare the performance of the AlexNet model with that of other models. The results obtained from the evaluation have been assessed to determine the effectiveness of the AlexNet model in the automated grading of KOA. The results of the study indicate that the AlexNet model demonstrates promising performance in classifying the severity of KOA. Comparative analysis reveals that the AlexNet model outperforms other models in terms of accuracy and efficiency. The evaluation of the model's performance provides valuable insights into the effectiveness of deep learning techniques for automated grading of KOA. This study highlights the significance of automated grading in the diagnosis and management of knee osteoarthritis. The utilization of deep learning technology, particularly the AlexNet model, shows promise in accurately classifying the severity of KOA. The findings suggest that automated grading methods can serve as valuable tools for healthcare professionals in assessing the progression of KOA and providing appropriate medical care. Further research and development in this area can contribute to enhancing the efficiency and accuracy of automated grading systems for KOA.

A Predictive Modeling for Characterization and Grading of Knee Osteoarthritis Using Machine Learning Algorithms: A Study in Early Diagnosis and Prognosis

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.

POSTMARKETING, PROSPECTIVE, MULTICENTRE, SINGLE-ARM STUDY OF SUBJECTS WHO RECEIVE AN INJECTION OF A MEDICAL DEVICE BASED ON HYALURONIC ACID: CLINICAL STUDY REPORT

Introduction: Viscosupplementation with an intra-articular injection of hyaluronic acid (HA) is widely used around the globe for pain management in patients with osteoarthritis (OA). Safety and clinical outcomes are debated for decades. First, products have been designed for multiinjections (3–5 injections at 1-week intervals); newer products and treatment schemes are developing continuously. Elevated concentration or dose, additional components, and chemical bonds can provide better and prolonged effects of treatment. All these features could give an advantage like single-injection treatment, prolonged time between injections and better pain management. With the use of a single injection one can get advantages such as the reduction of visits to the doctor and less interventions with their associated risks. For this purpose, a benefit/risk profile of the novel HA formulation was investigated. This study contributes to knee osteoarthritis (KOA) treatment.&#x0D; Methods: Postmarketing, prospective, multicentre, single-arm study of subjects with knee OA grades II to IV according to the Kellgren and Lawrence classification was used who received a single injection of medical HA-based device was performed. The study has been scheduled in the form of Visit 1 (month 0), Visit 2 (month 1), Visit 3 (month 2), Visit 4 (month 3), Visit 5 (month 4), Visit 6 (month 5) and Visit 7 (month 6). The changes in the WOMAC questionnaire have been evaluated upon treatment initiation for up to 6 months. The incidences of adverse events have been recorded throughout the study.&#x0D; Results: The clinical study was conducted in Ukraine and Poland by 5 clinical sites; 55 persons who met the study inclusion criteria were involved in the investigation. The age of subjects ranged from 36 to 80 years with mean age of 52.02 years. The score of the WOMAC questionnaire findings resulted in the improvement of pain and function after 1, 2, 3, 4, 5, 6 months compared to the population's baseline data (mean reduction from baseline 13.62, 18.31, 22.79, 22.18, 21.25, 21.55 points, respectively (p&lt;0.001)).&#x0D; Conclusion: This study showed HA's tolerability and safety in a single-injection application for at least 24 weeks, resulting in a promising treatment option for patients with KOA for whom conventional therapy has failed.

Deep Learning Based Knee Osteoarthritis Detection and Classification

Knee osteoarthritis (OA) is a musculoskeletal disease that significantly affects patients' quality of life. Early and accurate diagnosis of knee osteoarthritis is important for timely intervention and development of appropriate treatment plans. In this study, we present a novel approach using artificial intelligence (AI)-based deep learning to solve the problem of knee joint localization, joint width (JSW) area, and grading of knee OA from digital radiographs. The main aim of this study is to investigate the effectiveness of the deep learning method in predicting knee OA severity based on the Kellgren Lawrence (KL) rating. To achieve this, we developed a custom tool that uses neural networks (CNN) to analyze digital radiographs of the knee joint. Our approach focuses on identifying the presence of knee OA and measuring its severity according to the KL score.

INTRA-ARTICULAR PLATELET RICH PLASMA IN THE TREATMENT OF PATIENTS WITH KNEE OSTEOARTHRITIS

Introduction: Osteoarthritis of knee is the most common worldwide degenerative joint disease and is a common condition associated with pain and morbidity which significantly impacts the patients mobility and quality of life. It is treated by use of exercises, weight reduction, walking supports, bracing, topical and oral NSAID, intra-articular (IA) injections of corticosteroids and blood-derived products, including platelet-rich-plasma. An autologous blood product containing a high percentage of various growth factors (GFs), cytokines and modulating factors as PRP has shown promising results in achieving this goal. The aim of this prospective study was to evaluate the efficacy and safety of Platelet Rich Plasma (PRP) injections in patients affected by knee osteoarthritis (KOA) of grades from 1 to 3 Kellgren‑Lawrence (KL) grading. Materials and Methods: In this study a total of 30 patients, radiologically confirmed with knee osteoarthritis. All patients were managed with intra-articular platelet rich plasma injection. The pain and function of the target knee were evaluated by the VAS, KSS and IKDC scales at the baseline, 1 week, 6 weeks, 12 weeks, 18 weeks, 24 weeks and 48 weeks after treatment. Results: In this study, there was reduction of 5 ± 0.4 and 4 in mean VAS from baseline was found after 24 and 48 weeks after treatment. A significant function improvements of 32.10±1.5 and 21.4±4.9 in mean KSS from baseline was seen after 24 and 48 weeks respectively after treatment. Similarly a significant function improvements of 47.1±3.9 and 32.1±4.6 in mean IKDC from baseline was seen after 24 and 48 weeks respectively after treatment. Conclusion: Intra articular platelet rich plasma produce superior outcomes for symptomatic management in the treatment of patients with knee osteoarthritis grade I, II and III, including improved pain management, less joint stiffness and better participation in daily activity.