Assessment Of Knee Osteoarthritis Research Articles

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 < 0.01). The correlation coefficient between X-ray KL knee OA grades and WOMAC was moderate (R: 50.9%, P < 0.01), whereas the correlation coefficient between X-ray KL knee OA grades and KHIUS was comparatively stronger (R: 75.7%, P < 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.

Effects of three-dimensional femur and tibia postures on the parameters of standing long-leg radiographs for osteoarthritic knees in elderly female subjects

BackgroundLong-leg frontal radiographs of the lower extremities are used to assess knee osteoarthritis. Given the three-dimensional (3D) nature of alignment changes in osteoarthritis, postural alterations in the femur and tibia extend beyond the coronal plane (in-plane) to include the transverse and sagittal planes (out-of-plane). This study investigates the impact of these out-of-plane factors on in-plane knee alignment parameters observed in frontal radiographs. MethodsA total of 97 osteoarthritic knees in women were examined. Using a 3D-to-two-dimensional (2D) image matching technique, we evaluated the 3D postures of the femur and tibia in the standing position as viewed from frontal radiographs in the world coordinate system. Statistical analyses were conducted to explore associations between these 3D postures and 2D alignment parameters obtained from frontal radiographs under identical conditions. FindingsThe femur exhibited a medial inclination of 2.7°, a posterior inclination of 3.9°, and an internal rotation of 4.2°, whereas the tibia showed a lateral inclination of 6.4°, an anterior inclination of 6.7°, and an internal rotation of 6.7°. Both coronal and rotational postures of femur and tibia influenced the hip-knee-ankle angle, mechanical axis percentage, and medial proximal tibial angle. However, only coronal factors of tibia impacted tibial joint line obliquity relative to the floor. InterpretationAttention should be paid to the potential impact of the out-of-plane postures of the femur and tibia on parameters assessed in plain frontal radiographs of the lower extremities.

MedKnee: A New Deep Learning-Based Software for Automated Prediction of Radiographic Knee Osteoarthritis.

In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called "MedKnee" is developed to assist physicians in the diagnosis process of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. To accomplish this task, 5000 knee X-ray images obtained from the Osteoarthritis Initiative public dataset (OAI) were divided into train, valid, and test datasets in a ratio of 7:1:2 with a balanced distribution across each KL grade. The pre-trained Xception model is used for transfer learning and then deployed in a Graphical User Interface (GUI) developed with Tkinter and Python. The suggested software was validated on an external public database, Medical Expert, and compared with a rheumatologist's diagnosis on a local database, with the involvement of a radiologist for arbitration. The MedKnee achieved an accuracy of 95.36% when tested on Medical Expert-I and 94.94% on Medical Expert-II. In the local dataset, the developed tool and the rheumatologist agreed on 23 images out of 30 images (74%). The MedKnee's satisfactory performance makes it an effective assistant for doctors in the assessment of knee osteoarthritis.

Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images.

Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability. To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome. Retrospective. 194 OA progressors (mean age, 62 ± 9 years) and 406 controls (mean age, 61 ± 9 years) from the OA Initiative were randomly divided into training (80%) and testing (20%) cohorts. Sagittal 2D IW-TSE-FS (IW) and 3D-DESS-WE (DESS) at 3T. Anatomical subregions of cartilage, subchondral bone, meniscus, and the infrapatellar fat pad at baseline, 12-month, and 24-month were automatically segmented and served as inputs to form compartment-based graphs for a TRGCN model, which containing both regional and temporal information. The performance of models based on (i) clinical variables alone, (ii) radiologist score alone, (iii) combined features (containing i and ii), (iv) composite TRGCN (combining TRGCN, i and ii), (v) radiomics features, (vi) convolutional neural network based on Densenet-169 were compared. DeLong test was performed to compare the areas under the ROC curve (AUC) of all models. Additionally, interpretability analysis was done to evaluate the contributions of individual regions. A P value <0.05 was considered significant. The composite TRGCN outperformed all other models with AUCs of 0.841 (DESS) and 0.856 (IW) in the testing cohort (all P < 0.05). Interpretability analysis highlighted cartilage's importance over other structures (42%-45%), tibiofemoral joint's (TFJ) dominance over patellofemoral joint (PFJ) (58%-67% vs. 12%-37%), and importance scores changes in compartments over time (TFJ vs. PFJ: baseline: 44% vs. 43%, 12-month: 52% vs. 39%, 24-month: 31% vs. 48%). The composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression. Stage 2.

Evaluation of Knee Radiograph and its Interrelation between Body Mass Index and Range of Motion in Osteoarthritic Knee

Abstract Context: Assessing knee osteoarthritis (OA) radiologically is a well-recognised investigation technique. Range of motion (ROM) measurement gives an idea about the functional status of the joint. Body mass index (BMI) increment is one of the risk factors of worsening knee OA. Aim: The aim of this study was to evaluate OA radiologically and to search the status of BMI, ROM and Kellgren–Lawrence (KL) score of osteoarthritic knee patients and find their interrelations. Settings: This study was conducted at the department of physical medicine and rehabilitation in a tertiary hospital of Bangladesh. Designs: This was a cross-sectional study. Methodology: Purposive selection of 66 primary OA knees was done. Compartmental distribution of osteoarthritic knee was determined. Radiographics were determined by KL score system. BMI was calculated conventionally. Maximum flexion, extension and total excursion were measured by a universal goniometer. Results: Fifty per cent of participants had tricompartmental OA. Patients with maximum KL score 2 had significantly more total excursion (127.44 ± 2.89) compared to maximum KL score 3 (121.51 ± 3.14) and 4 (110.00 ± 3.58). Subjects with normal BMI had significantly more total excursion (125.00 ± 4.21) compared to overweight (121.03 ± 6.84) and obese patients (122.54 ± 4.31) (P = 0.044). 60.9% of subjects with normal BMI had significantly more maximum KL score 2 compared to overweight (33.3%) and obese patients (7.7%). A positive correlation was present between BMI and maximal KL score (r = 0.345, P = 0.005). A significant strong negative correlation was found between maximal KL score and total excursion (r = −0.825, P &lt; 0.001). Conclusion: Different percentages of unicompartmental, bicompartmental and tricompartmental knee OA were found. Osteoarthritic patients with higher BMI had lower ROM and increased KL radiographic scores.

Decreased Risk of Knee Osteoarthritis with Taller Height in an East Asian Population: A Nationwide Cohort Study.

Numerous studies have explored factors impacting osteoarthritis (OA), but its relationship with height remains uncertain. This study investigates the relationship between height and osteoarthritis risk in South Korea. Participants aged 50 or older who underwent health screenings in 2009 were selected from the National Health Insurance System database. A total of 1,138,904 subjects were divided into height quartiles (Q1-Q4) based on age and gender. Cox proportional hazard models were used to assess knee osteoarthritis incidence risk, with the shortest quartile (Q1) as the reference. After adjusting for age, sex, income, smoking, drinking, exercise, hypertension, diabetes mellitus, dyslipidemia, and body mass index (BMI), no significant difference in OA incidence risk based on height was observed. However, when adjusted for weight instead of BMI, we observed a gradual decrease in hazard ratio with increasing height. The hazard ratio for the tallest group was 0.787 (95% CI, 0.781~0.795). Similar results were obtained in all subgroups. Compared to previous studies, our findings present a clear distinction. Therefore, there may be racial differences in the association between height and knee OA risk, and our study provides evidence that, in East Asian populations, taller individuals have a reduced risk of knee OA.

Cross-cultural adaptation and validation of the KOOS, JR questionnaire for assessing knee osteoarthritis in Spanish-speaking patients.

The present study aims to translate, adapt and validate a Spanish version of the Knee Injury and Osteoarthritis Outcome Score, Joint Replacement (KOOS, JR), including a reliability and validity analysis in patients with knee osteoarthritis (OA). This study conducted a prospective validation study following the six stages of the "Guidelines for the Process of Cross-Cultural Adaptation of Self-Report Measures". Psychometric testing was conducted in patients with knee osteoarthritis. Subjects answered the Spanish KOOS, JR (S-KOOS, JR) and a validated Spanish Oxford Knee Score (S-OKS). Retest was conducted at 10days. Acceptability, floor and ceiling effect, internal consistency (Cronbach's α), reproducibility (mixed-effect model coefficient [MEMC]) and construct validity (Spearman's correlation; p = 0.05) were assessed. Forty-one patients (mean age: 65.6 ± 5.39; 48.8% female) participated in the study. All patients (100%) answered both scores during the first assessment and 38 (92.7%) during the second assessment. All patient-reported outcomes measures were answered completely (100%). The S-KOOS, JR resulted in 100% acceptability when answered. There were no ceiling or floor effects detected. The Cronbach's α for the S-KOOS, JR was 0.927 and its MEMC was 0.852 (CI 95% 0.636-1.078). The Spearman's correlation between the S-KOOS, JR and the S-OKS was 0.711 (CI 0.345-0.608; p < 0.001) and 0.870 (CI 0.444-0.651; p < 0.001) for the first and second assessments, respectively. The S-KOOS, JR has very high internal consistency and reproducibility, with a high correlation with the S-OKS; it is a reliable and valid instrument for characterising Spanish-speaking patients suffering from knee OA. IV.

Chinese guidelines for the rehabilitation treatment of knee osteoarthritis: An CSPMR evidence-based practice guideline.

Knee osteoarthritis (KOA) is the most common degenerative joint disease in China, causing a huge economic burden on patients, families, and society. Standardized KOA rehabilitation treatment is an important means to prevent and treat the disease and promote the development of high-quality medical services. This guideline is updated on the basis of the 2016 and 2019 editions. Clinical questions regarding rehabilitation assessment and treatment were selected through clinical questions screening and deconstruction, and multiple rounds of Delphi questionnaire consultation. The International Classification of Functioning, Disability and Health (ICF) was used as the theoretical framework, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method was used to grade the quality of evidence and recommendations. The reporting of this guideline followed the standard of Reporting Items for Practice Guidelines in Healthcare (RIGHT). Taking into account patients' preferences and values and the needs of Chinese clinical practice, a total of 11 clinical questions and 28 recommendations were established. The clinical questions were grouped into two categories: KOA assessment (body function, body structure, activity and participation, quality of life, and environmental factors and clinical outcomes assessment, resulting in 9 recommendations) and KOA treatment (health education, therapeutic exercise, therapeutic modalities, occupational therapy, assistive devices, and regenerative rehabilitation approaches, resulting in 19 recommendations). This is the first evidence-based guideline for KOA rehabilitation in China utilizing the ICF framework. This guideline provides key guidance for developing systematic, standardized, and precise rehabilitation protocols for KOA across various healthcare settings.

Association of adipokines with severity of knee osteoarthritis assessed clinically and on magnetic resonance imaging

ObjectivesWe aimed to evaluate the association between the adipokines: Leptin, Adiponectin, Resistin, and high sensitive-C-reactive protein (hs-CRP) with clinical, radiographical and magnetic resonance imaging (MRI) assessment of knee osteoarthritis (OA) severity. DesignWe performed a cross-sectional study in participants with earlier knee OA. Demographics, clinical (WOMAC), radiographical and MRI (BLOKS scoring) severity of knee OA were assessed. Serum leptin, adiponectin, resistin and hs-CRP were measured. Association of adipokines and hs-CRP with clinical, radiographic and MRI severity outcomes were evaluated using regression models with adjustment with age, sex, and body mass index (BMI). Results137 participants with earlier knee OA (82% women, mean ​± ​SD age: 55.5 ​± ​7.8 years) were included. Participants had moderate knee OA symptoms, mean WOMAC pain and function were 30.6 ​± ​18.0, and 31.7 ​± ​19.8 respectively. Mean BMI was 27.0 ​± ​5.9 ​kg/m2. After adjustment with age, sex and BMI, serum leptin was positively associated with osteophyte size, cartilage integrity, infrapatellar synovitis and effusion. While hs-CRP was associated with meniscus extrusion and adiponectin was associated with WOMAC pain and function. ConclusionSerum adipokines, particularly leptin was associated with severity of various structural defects of the knee joint on MRI beyond age, sex and BMI in earlier knee OA.

Prediction of total knee replacement using deep learning analysis of knee MRI

Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to make robust and accurate outcome predictions. Deep learning (DL) risk assessment models were developed to predict the progression of knee OA to total knee replacement (TKR) over a 108-month follow-up period using baseline knee MRI. Participants of our retrospective study consisted of 353 case–control pairs of subjects from the Osteoarthritis Initiative with and without TKR over a 108-month follow-up period matched according to age, sex, ethnicity, and body mass index. A traditional risk assessment model was created to predict TKR using baseline clinical risk factors. DL models were created to predict TKR using baseline knee radiographs and MRI. All DL models had significantly higher (p < 0.001) AUCs than the traditional model. The MRI and radiograph ensemble model and MRI ensemble model (where TKR risk predicted by several contrast-specific DL models were averaged to get the ensemble TKR risk prediction) had the highest AUCs of 0.90 (80% sensitivity and 85% specificity) and 0.89 (79% sensitivity and 86% specificity), respectively, which were significantly higher (p < 0.05) than the AUCs of the radiograph and multiple MRI models (where the DL models were trained to predict TKR risk using single contrast or 2 contrasts together as input). DL models using baseline MRI had a higher diagnostic performance for predicting TKR than a traditional model using baseline clinical risk factors and a DL model using baseline knee radiographs.

Is there any correlation between physical activity and severity of knee osteoarthritis in secondary referral hospital in Indonesia?

Introduction and Aim: Osteoarthritis is a pathological condition of the joints that is degenerative and progressive, affecting two-thirds of people over 65 years of age, with a prevalence of 1.39 million among males and 1.69 million in females. The main clinical manifestation of osteoarthritis is joint pain. The incidence and progression of knee osteoarthritis are strongly influenced by risk factors, consisting of systemic factors including age, sex, hormonal factors, and biomechanical factors such as obesity, physical activity, and muscle weakness. The purpose of this study was to determine if there was a relationship between physical activity, the severity of knee osteoarthritis and the level of pain based on the Kellgren-Lawrence system in gender. Materials and Methods: This research was conducted in a cross-sectional design with 26 participants at Baptis Hospital in Batu City. As references for the interview, the GPPAQ and the WOMAC questionnaire were used. The Kellgren-Lawrence system is used to assess knee osteoarthritis. Results: The Spearman correlation test results showed no significant relationship between physical activity and the severity of knee osteoarthritis, and no significance between physical activity and the degree of pain in general or by gender. Conclusion: There was no statistically significant relationship between physical activity with the severity of knee osteoarthritis and the level of pain in males and females patients at Baptis Hospital in Batu City.

Cluster Analysis of Biomechanical Gait Data and Pain Score as a Potential Classification of Severity in Knee Osteoarthritis

Osteoarthritis (OA) is the most common type of arthritis affecting approximately 240 million people globally, with increasing prevalence with age. The knee is the most prevalent joint affected by OA and it causes physical disability and decreased motor function which consequently affects the activity of daily living including mobility. Pain is the main symptom that is characterized in OA, which is measured using self-rated scales or questionnaires to determine several aspects of the pain including the intensity, frequency, and pattern. Quantifying pain is a standard clinical practice to diagnose and monitor symptomatic OA, however, its application for severity assessment is not well explored. To date, the severity assessment of knee OA is only by radiographic severity assessment that does not necessarily reflect the symptomatic OA. In this study, gait analysis was performed on symptomatic knee OA patients. Distinctive gait kinematic features were extracted using principal component analysis (PCA). Pain score and the gait features including spatiotemporal and kinematics were used for clustering analysis. Two clustering algorithms, K-means and K-medoids were conducted to cluster samples with similar features to assess knee OA characterization. The clustering solutions were evaluated based on three measures which are the Davies Bouldin index, Calinzki Harabasz index, and Silhouette index. This study discovered that majority of the datasets which is 5 out of 9 datasets had the best performance (fulfill at least 2 out of 3 performance index criteria) when the number of clusters, k is 4 and using the k-means algorithm. These clustering models can be used in the future as the labeling class of symptomatic knee OA that is based on pain and gait characteristics of knee OA. Future studies are suggested to test other pain assessment scores, include other gait features such as kinetic and muscle activity features, and employ various types of feature selection methods to improve the clustering performance.

Finite element modeling with subject-specific mechanical properties to assess knee osteoarthritis initiation and progression.

Finite element models of the knee can be used to identify regions at risk of mechanical failure in studies of osteoarthritis. Models of the knee often implement joint geometry obtained from magnetic resonance imaging (MRI) or gait kinematics from motion capture to increase model specificity for a given subject. However, differences exist in cartilage material properties regionally as well as between subjects. This paper presents a method to create subject-specific finite element models of the knee that assigns cartilage material properties from T2 relaxometry. We compared our T2 -refined model to identical models with homogeneous material properties. When tested on three subjects from the Osteoarthritis Initiative data set, we found the T2 -refined models estimated higher principal stresses and shear strains in most cartilage regions and corresponded better to increases in KL grade in follow-ups compared to their corresponding homogeneous material models. Measures of cumulative stress within regions of a T2 -refined model also correlated better with the region's cartilage morphology MRI Osteoarthritis Knee Score as compared with the homogeneous model. We conclude that spatially heterogeneous T2 -refined material properties improve the subject-specificity of finite element models compared to homogeneous material properties in osteoarthritis progression studies. Statement of Clinical Significance: T2 -refined material properties can improve subject-specific finite element model assessments of cartilage degeneration.

Virtual monoenergetic images from photon-counting spectral computed tomography to assess knee osteoarthritis

BackgroundDual-energy computed tomography has shown a great interest for musculoskeletal pathologies. Photon-counting spectral computed tomography (PCSCT) can acquire data in multiple energy bins with the potential to increase contrast, especially for soft tissues. Our objectives were to assess the value of PCSST to characterise cartilage and to extract quantitative measures of subchondral bone integrity.MethodsSeven excised human knees (3 males and 4 females; 4 normal and 3 with osteoarthritis; age 80.6 ± 14 years, mean ± standard deviation) were scanned using a clinical PCSCT prototype scanner. Tomographic image reconstruction was performed after Compton/photoelectric decomposition. Virtual monoenergetic images were generated from 40 keV to 110 keV every 10 keV (cubic voxel size 250 × 250 × 250 μm3). After selecting an optimal virtual monoenergetic image, we analysed the grey level histograms of different tissues and extracted quantitative measurements on bone cysts.ResultsThe optimal monoenergetic images were obtained for 60 keV and 70 keV. Visual inspection revealed that these images provide sufficient spatial resolution and soft-tissue contrast to characterise surfaces, disruption, calcification of cartilage, bone osteophytes, and bone cysts. Analysis of attenuation versus energy revealed different energy fingerprint according to tissues. The volumes and numbers of bone cyst were quantified.ConclusionsVirtual monoenergetic images may provide direct visualisation of both cartilage and bone details. Thus, unenhanced PCSCT appears to be a new modality for characterising the knee joint with the potential to increase the diagnostic capability of computed tomography for joint diseases and osteoarthritis.

Radiographic vs. MRI vs. arthroscopic assessment and grading of knee osteoarthritis - are we using appropriate imaging?

PurposeRadiographs and MRI scans are commonly used imaging techniques in the assessment of knee osteoarthritis. However, it currently remains uncertain how good a representation of the actual condition of the knee joint these investigations provide. By comparing them against arthroscopic findings the aim of our study was to conclude how accurate these imaging techniques are at grading knee osteoarthritis.MethodsThis was a retrospective study looking at knee arthroscopies performed at a tertiary centre over a 5 year period. The Outerbridge grade given at arthroscopy was correlated with pre-operative radiograph and MRI scores, so as to assess the reliability of these imaging techniques at predicting the actual severity of knee osteoarthritis seen.ResultsKellgren-Lawrence (KL) grading of radiographs was moderately correlated with Outerbridge grades from arthroscopy for the medial compartment of the knee (Spearman’s rho (SR) 0.483, p < 0.001), with a milder correlation in the lateral compartment (SR 0.218, p = 0.003). MRI reporting of knee osteoarthritis was moderately correlated with Outerbridge grades in the medial compartment (SR 0.451, p < 0.001), mildly correlated for both the lateral (SR 0.299, p < 0.001) and patellofemoral joint compartments (SR 0.142, p = 0.054). KL and MRI grading was moderately correlated for the medial compartment (SR 0.475, p < 0.001) and mildly correlated for the lateral compartment (SR 0.277, p < 0.001).ConclusionThe ability of radiographs to represent the actual condition of knee osteoarthritis is underestimated. KL grading especially best represents the disease seen in the medial compartment of the knee joint, with a moderate correlation to Outerbridge scores given on arthroscopic assessment. We suggest that whilst MRI is a useful tool in the investigation of knee symptoms, it is often unnecessarily used in patients with OA, when in fact, radiographs alone would be sufficient.Evidence levelIII

3-D JOINT SPACE WIDTH FROM WEIGHT BEARING COMPUTED TOMOGRAPHY IS BETTER THAN 2-D RADIOGRAPHIC MEASURES AT PREDICTING FUTURE LATERAL JOINT SPACE NARROWING AT THE KNEE: A MULTICENTER OSTEOARTHRITIS STUDY INVESTIGATION

3-D joint space width (JSW) measured from weight bearing CT (WBCT) has been suggested to be more sensitive than radiographic 2-D measurement in detecting joint space narrowing (JSN), but its diagnostic value in osteoarthritis is yet to be established. To determine the predictive validity of 3-D JSW measurement from baseline WBCT compared to 2-D radiographic measures in identifying structural disease progression at the knee. WBCT knee imaging was acquired at the 144-month visit of the Multicenter Osteoarthritis Study (MOST) with radiographic Kellgren and Lawrence grade (KLG), medial and lateral tibiofemoral radiographic OARSI JSN grades. Progression in medial and lateral JSN grade was recorded 2 years subsequent. After semi-automatic segmentation of 663 available knees, joint space mapping was performed to create 3-D JSW maps. A template was registered to each individual joint surface, a 3-D statistical shape model created, and JSW measurements transferred to the template. A statistical parametric mapping (SPM) general linear model adjusted for age, sex, BMI, and the first 5 shape modes (controlling for effects of systematic misregistration) was used to test the dependence of baseline 3-D JSW on baseline medial and lateral JSN in turn. One knee per participant was included in the SPM model, when required selecting side according to worst baseline compartmental JSN grade or randomly if equal. Mean JSW values from significantly narrower SPM ROIs were taken for all knees and used in receiver operating characteristic analysis to deliver areas under the curve (AUC) in a leave-one-out cross-validation classifier predictive model for future worsening of medial and lateral JSN at 2 years. This prediction model also tested baseline KLG, medial and lateral JSN grade. All predictive models were controlled by inclusion of age, sex, and BMI and compared against each other. 10 knees did not have radiographic grading, so the study set consisted of 653 knees from 394 individuals. 218 participants were female, mean ± SD age was 63.5 ± 9.6 years, mass 82.7 ± 17.7 kg, height 170 ± 9 cm, and BMI 28. 5 ± 5.0 kg/m 2 . SPM revealed regions of significantly narrower baseline medial JSW up to ∼0.75 mm per increment in medial JSN grade (unmasked medial compartment red zone in fig. 1) and narrower baseline lateral JSW up to ∼1 mm per increment in baseline lateral JSN grade (unmasked lateral compartment red zone in fig. 2). Mean medial and lateral ROI JSW values were taken at all available knees for predictive modelling. AUC values with 95% confidence limits showed that baseline mean medial ROI JSW (0.68, 0.61-0.74) was worse than baseline KLG (0.75, 0.68-0.81) and no better than medial JSN (0.73, 0.65-0.79) or lateral JSN (0.66, 0.6-0.71) in predicting future medial JSN. However, baseline mean lateral ROI JSW (0.84, 0.72-0.91) was better than medial JSN (0.61, 0.49-0.72) and lateral JSN (0.71, 0.54-0.86) in prediction of future lateral JSN. It was also better than baseline KLG (0.77, 0.76-0.86) but not outside the 95% confidence limits, however combining baseline lateral ROI JSW and KLG in the model further improved the AUC (0.87, 0.78-0.92) beyond the limits for just KLG (0.77, 0.76-0.86). Baseline 3-D lateral compartment JSW from WBCT is better than 2-D radiographic measures at predicting progression in lateral compartment JSN, but the same was not seen for medial JSN. This suggests an important role for 3-D weight bearing JSW in assessment of knee osteoarthritis according to disease phenotype. National Institutes of Health, University of Kansas (R01AR071648), University of Iowa (U01AG18832) and University of California-San Francisco (U01AG19069). NS is a consultant for Integra BioLife, Trice Medical and Pacira Biosciences. The authors would like to thank participants and staff of the MOST study. CORRESPONDENCE ADDRESS: tom@turmezei.com

The Updated Role of Ultrasound in the Assessment of Knee Osteoarthritis

Abstract Background Osteoarthritis (OA) of the knee is the most common form of knee arthritis and a leading cause of chronic disability. Objective The aim of the current study is to reassess the utility of the updated ultrasound in the patients with knee osteoarthritis and outline its clinical application. Patients and Methods The studied group included 36 patient 23females and 13 males with their ages ranged between 39 and 58 years (average age 44 years). The patients were referred to US examination fulfilling the ACR clinical criteria for knee OA after orthopedic and /or rheumatologist consultation. The study was performed in Radiodiagnosis department Ain Shams University Hospitals (20 patients ) and at one private center (16 patients). Results There was a discrepancy between the results obtained by clinical examination and those demonstrated by ultrasonography. Clinical examination detected 22 (61.1%) of our cases. Prevalence of US findings in our cases were femoral articular degeneration in 28 (77.7%) patients, Tibiofemoral osteophytes were seen in 26 patients (72.2%), knee effusion in 22 patients (61.1%),meniscal extrusion in 22 patients (61.1%) meniscal degeneration in 17 patients (47.2%) and synovial hypertrophy in 15patients (41.6%). Baker’s cysts were demonstrated in 14patients (38.8%) while pes anserine syndrome was demonstrated in 4 cases. Meniscal degeneration and meniscal extrusion were correlated significantly with femoral cartilage degeneration (P&amp;lt;.001). Although knee effusion did not correlate with advanced knee effusion did significantly (P &amp;gt; 0.05). Baker's cysts is statistically related to the presence and severity of mensical changes and also related to the degree of femoral articular cartilage degeneration. Conclusion US is a valuable technique that can assess soft tissue structures within the knee and their involvement in the osteoarthritic process .US enables in guiding and monitoring therapy through detection of knee structural damage.

Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping

Segmentation of knee articular cartilage tissue (ACT) from 3D magnetic resonance images (MRIs) is a fundamental task in assessing knee osteoarthritis (KOA). However, automated ACT segmentation of knee cartilage is complicated by (1) the variability of pathological structures in terms of shape, size, spatial resolution and (2) the uncertainties in delineating inter- and intra-cartilage boundaries (possibly touching inter-cartilage surfaces, intensity inhomogeneity, cartilage loss due to disease progression). To address these problems, we propose a novel joint deep and hand-crafted learning-based (JD-HCl) framework. The fully automated segmentation pipeline is a combination of three main steps: 3D convolutional neural networks (CNNs), diffeomorphic mapping, and a conventional hand-crafted feature-based classification model. Firstly, the initial segmentation of knee ACT was drawn from preprocessed knee MRIs using 3D U-Net. Secondly, the spatial alignment in the image domain was carried out using diffeomorphic image registration derived from an estimate of the anatomical correspondences between the subjects and population-specific template. Finally, a tissue-specific hand-crafted feature set was extracted from the spatially aligned pre-segmented ACT region and combined with the semantic context priors obtained from 3D CNN model to develop the second-stage learning model. The proposed method was evaluated with two publicly available Osteoarthritis Initiative (OAI) datasets and one in-house MRI sequences from the Alfred hospital, Melbourne, Australia. The proposed JD-HCl framework produced strong Dice similarity coefficient (DSC), ranging between 97.4% and 98.5% (95% confidence interval) for all cartilage compartments at baseline to 97.2% and 98.5% (the 1-year follow-up) for OAI(-Imorphics) dataset, and 89.3% and 94.6% for OAI-ZIB validation dataset, respectively. Average correlations of cartilage volumes between manual and automatic segmentations were 0.9 and 0.83 for OAI-ZIB dataset, respectively, and 0.975, 0.995, and 0.99 for OAI(-Imorphics) validation datasets, respectively. In longitudinal studies, accurate segmentation of knee ACT using the proposed method produces reproducible cartilage volume, thickness measurements valuable for the study of KOA progression.

Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs

Knee osteoarthritis (KOA) is an orthopedic disorder with a substantial impact on mobility and quality of life. An accurate assessment of the KOA levels is imperative in prioritizing meaningful patient care. Quantifying osteoarthritis features such as osteophytes and joint space narrowing (JSN) from low-resolution images (i.e., X-ray images) are mostly subjective. We implement an objective assessment and quantification of KOA to aid practitioners. In particular, we developed an interpretable ensemble of convolutional neural network (CNN) models consisting of three modules. First, we developed a scale-invariant and aspect ratio preserving model to localize Knee joints. Second, we created multiple instances of "hyperparameter optimized" CNN models with diversity and build an ensemble scoring system to assess the severity of KOA according to the Kellgren–Lawrence grading (KL) scale. Third, we provided visual explanations of the predictions by the ensemble model. We tested our models using a collection of 37,996 Knee joints from the Osteoarthritis Initiative (OAI) dataset. Our results show a superior (13–27%) performance improvement compared to the state-of-the-art methods.

Role of ultrasonography in evaluation of knee osteoarthritis

Objective To evaluate the role of ultrasonography (US) in assessing knee osteoarthritis (OA) compared with radiograph. Background Similar to renal or cardiac failures, OA is considered a common disorder in the joint as an organ. High-resolution US is used increasingly in musculoskeletal medicine owing to its value in the morphological and structural assessment of soft tissue in patients with arthritis. Patients and methods This study included 50 symptomatic knee joints from 50 patients (male/female24 / 26) with the clinical diagnosis of OA; all of them had knee pain and/or swelling. All patients underwent US examination on their knees. Weight-bearing anteroposterior and lateral knee radiographs were assessed for the Kellgren and Lawrence grading along with the potential presence of patellofemoral degenerative signs. Results US findings showed irregular articular surface and narrow joint space (72.0%), marginal osteophytes (64.0%), joint effusions (88.0%), decreased cartilage thickness (32.0%), synovial thickening (20.0%), meniscal lesions (70.0%), Baker's cyst (20.0%), and ligament and tendon injury (24.0%). Soft tissue changes could not be detected in radiograph (0.0%) but were identified clearly by the US in our study, with P value less than 0.001. Joint effusion, which was detected in 44 (88.0%) knees in the US only was detected in six (12.0%) knees in radiograph, with a P value less than 0.001. Kellgren and Lawrence grades (1–3) showed an increased incidence of joint effusion and meniscal lesion in the US, where all the grade 3 cases showed joint effusions and meniscal lesions. Conclusion US is a simple and reproducible technique for the assessment of periarticular and intra-articular abnormalities involved in the pathophysiology of knee OA.