Fully automatic quantification of knee osteoarthritis severity on plain radiographs

Ten-year trends in values of joint space width and osteophyte area of knee joints: Comparison of the baseline and fourth ROAD study surveys

Objective

Considering the joint space width and osteophyte area (OPA) of the knee joints of Japanese adults, this study elucidated the ten-year trends in medial minimum joint space width (mJSW) and OPA using data of two independent cohorts from a population-based cohort study.

Methods

The baseline survey of the Research on Osteoarthritis/Osteoporosis Against Disability study was conducted from 2005 to 2007; 2975 participants (1041 men, 1934 women) completed all knee osteoarthritis (OA) examinations. The fourth survey was performed from 2015 to 2016; distinct 2445 participants (764 men, 1681 women) completed identical examinations. The medial mJSW and medial tibial OPA were measured bilaterally using an automated system.

Results

The mean medial mJSW (standard deviation) was 3.22 (0.96) mm and 2.65 (0.95) mm at baseline and 3.81 (1.20) mm and 3.13 (1.15) mm in the fourth survey for men and women, respectively. The mean medial mJSW in the fourth survey was significantly greater in both men and women in all age groups than at baseline (p ​< ​0.01). The mean OPAs in men aged 40-49 and 60-69 years and women aged 40-49, 50-59, 60-69, and 70-79 years were significantly smaller in the fourth survey (p ​< ​0.05). The trend in mJSW remained the same even after adjusting for confounding factors in the multivariate analysis, but the trend in OPA was weakened.

Conclusions

A significant improvement in the medial mJSW within 10 years could decrease the incidence and progression of knee OA and prevent the risk of walking disability.

Diagnostic accuracy of a novel ultrasound imaging index for knee osteoarthritis: Evaluation of sensitivity, specificity, and predictive values

PURPOSE

We aimed to develop and validate a new ultrasonography (US) index for the diagnosis of primary medial-type knee osteoarthritis (OA).

METHODS

In total, 156 patients (203 limbs) underwent standing knee radiography and the US for suspected knee OA. Total osteophyte height (TOH) and distance between bones (DBB) aided diagnosis. Logistic regression identified optimal cutoff values. Thresholds from logistic regression informed recipient operating characteristic curve (ROC) analysis, balancing sensitivity and specificity. These thresholds were then applied in the differential thermal analysis (DTA) to construct a 2 × 2 table.

RESULTS

The TOH-DBB index showed that a DBB of 5.6 mm or less was required to diagnose primary medial-type knee arthropathy. The results in the 2 × 2 table were 41 true-positive (TP), 10 false negative (FN), 22 true-negative (TN), and 7 false positive (FP). A DBB of 5.6 mm or less and TOH of 4.7 mm or more were necessary to diagnose severe deformity. The results in the 2 × 2 table were 10 TP, 4 FN, 23 TN, and 4 FP.

CONCLUSION

The TOH-DBB index was confirmed to capture changes in primary medial-type knee OA across various stages.

DC-AAE: Dual channel adversarial autoencoder with multitask learning for KL-grade classification in knee radiographs

Knee osteoarthritis (OA) is a frequent musculoskeletal disorder that leads to physical disability in older adults. Manual OA assessment is performed via visual inspection, which is highly subjective as it suffers from moderate to high inter-observer variability. Many deep learning-based techniques have been proposed to address this issue. However, owing to the limited amount of labelled data, all existing solutions have limitations in terms of performance or the number of classes. This paper proposes a novel fully automatic Kellgren and Lawrence (KL) grade classification scheme in knee radiographs. We developed a semi-supervised multi-task learning-based approach that enables the exploitation of additional unlabelled data in an unsupervised as well as supervised manner. Specifically, we propose a dual-channel adversarial autoencoder, which is first trained in an unsupervised manner for reconstruction tasks only. To exploit the additional data in a supervised way, we propose a multi-task learning framework by introducing an auxiliary task. In particular, we use leg side identification as an auxiliary task, which allows the use of more datasets, e.g., CHECK dataset. The work demonstrates that the utilization of additional data can improve the primary task of KL-grade classification for which only limited labelled data is available. This semi-supervised learning essentially helps to improve the feature learning ability of our framework, which leads to improved performance for KL-grade classification. We rigorously evaluated our proposed model on the two largest publicly available datasets for various aspects, i.e., overall performance, the effect of additional unlabelled samples and auxiliary tasks, robustness analysis, and ablation study. The proposed model achieved the accuracy, precision, recall, and F1 score of 75.53%, 74.1%, 78.51%, and 75.34%, respectively. Furthermore, the experimental results show that the suggested model not only achieves state-of-the-art performance on two publicly available datasets but also exhibits remarkable robustness.

An accurate method for measuring knee joint space width despite variations in beam projection angles

BACKGROUND

Assessing knee osteoarthritis (OA) severity through joint space width (JSW) measurements can be difficult due to anatomical variations, beam projection angle (BPA) errors, and patient posture during X-rays. Although various methods address these issues, a consensus on the reference point for accurate measurement is lacking. Therefore, establishing a precise evaluation method for JSW is imperative.

METHODS

Simulation on 30 knees with advanced OA to measure the JSW using digital reconstruction radiographs from computed tomography (CT) images was conducted. The distance between the medial femoral condyle and the anterior and posterior borders of the medial tibial plateau (represented by DAB and DPB, respectively) and their average (AVD) were used to evaluate JSW. Discrepancies were analyzed for various BPAs. Additionally, the reliability of measuring JSW using DAB, DPB, and AVD was evaluated in a proof-of-concept study on 100 knees using anteroposterior X-rays at three BPAs (neutral, 5° caudal, and 5° cephalic tilt).

RESULTS

In the simulation study, the AVD method had discrepancies below 1 mm from BPA 0° of 5°, 10° caudal tilt, and 5° cephalic tilt (P = 0.066, P = 0.120, and P < 0.001, respectively). However, the values of DAB and DPB showed significant discrepancies from BPA 0° (all over 1 mm and all P < 0.001) for various BPAs. The AVD measurement demonstrated the least discrepancy in JSW measurements based on BPA variations compared with DAB and DPB methods in the proof-of-concept study.

CONCLUSIONS

A simple method for accurately measuring joint space width, even when X-rays are taken at unintended angles can be applied in clinical practice.

Proximal tibial osteotomy with absorbable spacer combined with fibular osteotomy has similar clinical outcomes to high tibial osteotomy in the treatment of knee osteoarthritis

PURPOSE

Proximal tibia osteotomy with absorbable spacer combined with fibular osteotomy (TPOASI) is an emerging surgical technique for treating knee osteoarthritis (KOA); however, the efficacy of this procedure remains unknown. We hypothesize that TPOASI can achieve similar clinical outcomes to opening-wedge high tibial osteotomy (OW-HTO). The objective of this study is to compare the clinical results between these two procedures.

METHODS

Patients who underwent TPOASI or OW-HTO from July 2016 to September 2020 were included. The following outcome parameters were determined before and after the surgery: the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the Knee Injury and Osteoarthritis Outcome Score (KOOS), the visual analogue scale of pain, the Intermittent and Persistent Osteoarthritis Pain Scale, femorotibial angle, and post-operative complications.

RESULTS

In total, 209 cases were analyzed (102 in TPOASI group; 107 in OW-HTO group) with 3.1 years average follow-up. Both procedures achieved significant improvement in KOOS (62.0 to 24.4 in the TPOASI and 62.8 to 26.2 in the OW-HTO group, p < 0.001) and WOMAC score (68.9 to 24.1 in the TPOASI versus 69.9 to 26.1 in the OW-HTO group, p < 0.001). There were no significant differences in complications or femorotibial angle between the two groups but the only significant difference in the outcome parameters was the WOMAC stiffness score (19.6 in the TPOASI versus 26.5 in the OW-HTO group).

CONCLUSION

TPOASI achieves comparable results to OW-HTO in terms of clinical scores, radiographic results, and complications, but has the advantage of avoiding internal fixation removal.

Statistical model of trajectories of reduced mobility in older people with locomotive disorders: a prospective cohort study with group-based trajectory model

BACKGROUND

Today we experience "Super-aged society", and a drastic increase in the number of older people needing assistance is an urgent matter for everyone from medical and socio-economical standpoints. Locomotive organ dysfunction due to musculoskeletal disorders is one of the main problems in these patients. Although the concepts of frailty and sarcopenia have been proposed for functional decline, pain remains the main and non-negligible complaint in these of such disorders. This prospective cohort study aimed to observe the changes of reduced mobility in patients with locomotive disorders and to determine the risk factors for functional deterioration of those patients using statistical modeling.

METHODS

A cohort of older adults with locomotive disorders who were followed up every 6 months for up to 18 months was organized. Pain, physical findings related to the lower extremities, locomotive function in performing daily tasks, and Geriatric Locomotive Function Scale-25 (GLFS-25) score were collected to predict the progress of deterioration. Group-based trajectory analysis was used to identify subgroups of changes of GLFS-25 scores, and multinomial logistic regression analysis was performed to investigate potential predictors of the GLFS-25 trajectories.

RESULTS

Overall, 314 participants aged between 65 and 93 years were included. The participants were treated with various combinations of orthopedic conservative treatments on an outpatient basis. The in-group trajectory model analysis revealed a clear differentiation between the four groups. The mild and severe groups generally maintained their GLFD-25 scores, while the moderate group included a fluctuating group and a no change group.　This study showed that comorbidity of osteoporosis was related to GLFS-25 score over 18 months. Age was a weak factor to be moderate or severe group, but gender was not. In addition, the number of pain locations, number of weak muscles, one-leg standing time, grip strength and BMI significantly contributed to the change in GLFS-25 score.

CONCLUSIONS

This study proposes an effective statistical model to monitor locomotive functions and related findings. Pain and comorbid osteoporosis are significant factors that related to functional deterioration of activities. In addition, the study shows a patient group recovers from the progression and their possible contributing factors.

Medial Knee Joint Space in Relation to Joint Function and Early Knee Pain in 20-45-Year Adults: A Cross Sectional Study

Background

Anterior knee pain is very common in the young and active age groups but there are no significant studies on the normal population. Therefore, the objective of the study was to understand the relation between medial knee joint space to the development of knee pain and functional disability with special reference to the 20-45-year age group.

Materials and methods

250 cases with mean age of 36.36 years presenting with knee pain for at least 1 month were included in this prospective study (July 2021 and June 2022) and were asked to self-complete questionnaires on knee pain which included KNEST, AKPQ, VAS and IPAQ. Patients underwent X-rays of bilateral knees in AP view with weight bearing and then medial and lateral knee joint spaces were calculated.

Results

There was a higher incidence of knee pain in women compared to men. The average medial and lateral joint space widths (MJSW and LJSW) of the right knee were 4.22 mm and 4.57 mm, respectively. For the left knee it was 4.19 mm and 4.42 mm, respectively. There is a decrease in MJSW with increasing age, level of pain and BMI. Also, with an increase in physical activity, there was a higher incidence of knee pain.

Conclusion

Overuse injuries are the main cause of knee pain in this age group. There is a significant association between medial joint space width and age, gender, level of pain, and BMI. Rising BMI patterns at a young age, leads to an increase in physical activity, which in turn leads to early knee pain and also predisposes to osteoarthritis.

Changes in knee joint destruction patterns among patients with rheumatoid arthritis undergoing total knee arthroplasty in recent decades

OBJECTIVES

This study aimed to investigate the trend of joint destruction patterns on knee radiographs of patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA) over the past 16 years.

METHOD

Medial joint space, lateral joint space, medial spur area, lateral spur area (L-spur), and femoro-tibial angle were obtained from 831 preoperative knee radiographs of patients with RA who underwent TKA between 2006 and 2021 using software capable of automatic measurements. Non-hierarchical clustering was performed based on these five parameters. Trends in the five individual radiographic parameters and the ratio of each cluster were investigated during the target period. Moreover, clinical data from 244 cases were compared among clusters to identify factors associated with this trend.

RESULTS

All parameters, except for L-spur, showed significant increasing trends from 2006 to 2021. The radiographs were clustered into groups according to the characteristic pattern of radiographic findings: cluster 1 (conventional RA type), with bicompartmental joint space narrowing (JSN), less spur formation, and valgus alignment; cluster 2 (osteoarthritis type), with medial JSN, medial osteophytes, and varus alignment; and cluster 3 (less destructive type), with mild bicompartmental JSN, less spur formation, and valgus alignment. The ratio of cluster 1 showed a significantly decreasing trend contrary to the significantly increasing trend in clusters 2 and 3. The DAS28-CRP of cluster 3 was higher than those of clusters 1 and 2.

CONCLUSIONS

Radiographs of TKA recipients with RA are increasingly presenting osteoarthritic features in recent decades. Key Points • Using automated measurement software, morphological parameters were measured from radiographs of 831 patients with rheumatoid arthritis who had undergone TKA in the past 16 years. • Cluster analysis based on the radiographic parameters revealed that the radiographs of patients with end-stage knee arthritis requiring total knee arthroplasty were classified into three groups. • In patients with rheumatoid arthritis who have undergone total knee arthroplasty in the past 16 years, the proportion of clusters with features of osteoarthritis and difficult-to-treat rheumatoid arthritis has increased, while the proportion of conventional rheumatoid arthritis has decreased.

Machine learning as an adjunct to expert observation in classification of radiographic knee osteoarthritis: findings from the Hertfordshire Cohort Study

BACKGROUND

Osteoarthritis is the most prevalent type of arthritis. Many approaches exist for characterising radiographic knee OA, including machine learning (ML).

AIMS

To examine Kellgren and Lawrence (K&L) scores from ML and expert observation, minimum joint space and osteophyte in relation to pain and function.

METHODS

Participants from the Hertfordshire Cohort Study, comprising individuals born in Hertfordshire from 1931 to 1939, were analysed. Radiographs were assessed by clinicians and ML (convolutional neural networks) for K&L scoring. Medial minimum joint space and osteophyte area were ascertained using the knee OA computer-aided diagnosis (KOACAD) program. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was administered. Receiver operating characteristic analysis was implemented for minimum joint space, osteophyte, and observer- and ML-derived K&L scores in relation to pain (WOMAC pain score > 0) and impaired function (WOMAC function score > 0).

RESULTS

359 participants (aged 71-80) were analysed. Among both sexes, discriminative capacity regarding pain and function was fairly high for observer-derived K&L scores [area under curve (AUC): 0.65 (95% CI 0.57, 0.72) to 0.70 (0.63, 0.77)]; results were similar among women for ML-derived K&L scores. Discriminative capacity was moderate among men for minimum joint space in relation to pain [0.60 (0.51, 0.67)] and function [0.62 (0.54, 0.69)]. AUC < 0.60 for other sex-specific associations.

DISCUSSION

Observer-derived K&L scores had higher discriminative capacity regarding pain and function compared to minimum joint space and osteophyte. Among women, discriminative capacity was similar for observer- and ML-derived K&L scores.

CONCLUSION

ML as an adjunct to expert observation for K&L scoring may be beneficial due to the efficiency and objectivity of ML.

An Automatic Method for Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis

Efficient and scalable early diagnostic methods for knee osteoarthritis are desired due to the disease’s prevalence. The current automatic methods for detecting osteoarthritis using plain radiographs struggle to identify the subjects with early-stage disease. Tibial spiking has been hypothesized as a feature of early knee osteoarthritis. Previous research has demonstrated an association between knee osteoarthritis and tibial spiking, but the connection to the early-stage disease has not been investigated. We study tibial spiking as a feature of early knee osteoarthritis. Additionally, we develop a deep learning based model for detecting tibial spiking from plain radiographs. We collected and graded 913 knee radiographs for tibial spiking. We conducted two experiments: experiments A and B. In experiment A, we compared the subjects with and without tibial spiking using Mann-Whitney U-test. Experiment B consisted of developing and validating an interpretative deep learning based method for predicting tibial spiking. The subjects with tibial spiking had more severe Kellgren-Lawrence grade, medial joint space narrowing, and osteophyte score in the lateral tibial compartment. The developed method achieved an accuracy of 0.869. We find tibial spiking a promising feature in knee osteoarthritis diagnosis. Furthermore, the detection can be automatized.

Radiological osteoarthritic knee joint changes in high school and collegiate sumo wrestlers: The observational study

We examined the effects of sumo on their knee joints, and investigated the relationship between radiological changes and knee joints symptoms, and the relationship between knee radiological changes and the physical characteristics of the wrestlers. Fifty-six high-school and 128 college freshman sumo wrestlers who belonged to the Japanese Sumo Federation. To evaluate radiological changes in the knee joints of high-school and college freshmen sumo wrestlers. They underwent routine radiographic examination of their knee joints and were instructed to answer a questionnaire regarding their knee symptoms as a medical check. The mean height, weight, body mass index (BMI), and sumo career/experience of the participants were 174.1 cm, 106.9 kg, 35.1 kg/m2, and 7.9 years, respectively. Twenty-five high-school (44.6%) and 54 collegiate (42.2%) sumo wrestlers had some knee symptoms, which was significantly associated with sumo career as a risk factor. Five high-school (8.9 %) and 18 collegiate (14.1 %) sumo wrestlers had joint space narrowing. Considering the relationship between knee symptoms and radiological changes, significant correlations between osteophyte formation and bony sclerosis and knee symptoms were observed. According to the Kellgren-Laurence (KL) classification, 7 high-school (12.5%) and 26 collegiate (20.3%) sumo wrestlers were grade 2, 3, or 4. The risk factors of degenerative radiographic changes in the knee joints of the participants were heavyweight, large BMI, and older age. The knee osteoarthritic changes had already appeared in 12.5% high-school sumo wrestlers at the admission.

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

Background

The 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.

Methods

Forty 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.

Results

Greater 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.

Conclusions

Weight-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.

A generalized deep learning framework for automatic rheumatoid arthritis severity grading

Knee rheumatoid arthritis (RA) is the highly prevalent, chronic, progressive condition in the world. To diagnose this disease in the early stage in detail analysis with magnetic resonance (MR) image is possible. The imaging modality feature allows unbiased assessment of joint space narrowing (JSN), cartilage volume, and other vital features. This provides a fine-grained RA severity evaluation of the knee, contrasted to the benchmark, and generally used Kellgren Lawrence (KL) assessment. In this research, an intelligent system is developed to predict KL grade from the knee dataset. Our approach is based on hybrid deep learning of 50 layers (ResNet50) with skip connections. The proposed approach also uses Adam optimizer to provide learning linearity in the training stage. Our approach yields KL grade and JSN for femoral and tibial tissue with lateral and medial compartments. Furthermore, the approach also yields area under curve (AUC) of 0.98, accuracy 96.85%, mean absolute error (MAE) 0.015, precision 98.31%, and other commonly used parameters for the existence of radiographic RA progression which is improved than the existing state-of-the-art.

Have the Radiographic Characteristics of Total Knee Arthroplasty Recipients in Rheumatoid Arthritis Changed After the Induction of Biologic Disease Modifying Anti-Rheumatic Drugs?

OBJECTIVES

Whether the characteristics of patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA) have changed in the era of biologic disease-modifying antirheumatic drugs (bDMARDs) is unclear. We compared the radiographic findings of the knees in TKA recipients with RA before and after the introduction of bDMARDs.

METHODS

Consecutive patients who underwent primary TKA between 1999 and 2002 (115 knees; 79 patients, group A) and between 2013 and 2017 (117 knees; 95 patients, group B) were retrospectively evaluated. Clinical data, including disease duration, medication, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and rheumatoid factor (RF), were collected. The Larsen classification, joint space narrowing (JSN), bone erosion, geode and osteophyte formation were evaluated on preoperative radiographs.

RESULTS

Osteophyte formation was significantly increased, and bone erosion and geode formation were significantly decreased in group B. In addition, medial-dominant JSN was significantly increased, and bicompartmental JSN was significantly decreased in group B. Medial-dominant JSN was positively, and bone erosion was negatively associated with osteophyte formation.

CONCLUSIONS

Following the introduction of bDMARDs, typical radiographic findings of rheumatoid knees have decreased, and secondary osteoarthritis (OA)-like changes, characterized by osteophyte formation and medial-dominant JSN, have increased in the knees of TKA recipients.

Superiority of Multiple-Joint Space Width over Minimum-Joint Space Width Approach in the Machine Learning for Radiographic Severity and Knee Osteoarthritis Progression

Simple Summary

Minimum-joint space width (JSW) is a prevalent clinical parameter in quantifying the joint space narrowing condition in knee osteoarthritis (KOA). In this study, we propose a novel multiple-JSW measurement, which is estimated by a deep learning-based model in an automated manner. The performance of the proposed automated measurement is found to be superior to the conventionally used minimum-JSW in the severity classification and progression prediction of KOA owing to the additional information of the joint space morphology encoded in the new approach. It is further demonstrated that the deep learning-based approach yields comparable performance as the measurement by radiologists. The approach presented in this work may lead to the development of a computer-aided tool for clinical practitioners that could facilitate the KOA diagnosis and prognosis with the fully automated, accurate, and efficient computation of the joint-space parameters.

Abstract

We compared the prediction efficiency of the multiple-joint space width (JSW) and the minimum-JSW on knee osteoarthritis (KOA) severity and progression by using a deep learning approach. A convolutional neural network (CNN) with ResU-Net architecture was developed for knee X-ray imaging segmentation and has attained a segmentation efficiency of 98.9% intersection over union (IoU) on the distal femur and proximal tibia. Later, by leveraging the image segmentation, the minimum and multiple-JSWs in the tibiofemoral joint were estimated and then validated by radiologist measurements in the Osteoarthritis Initiative (OAI) dataset using Pearson correlation and Bland–Altman plots. The agreement between the CNN-based estimation and radiologist’s measurement of minimum-JSWs reached 0.7801 (p < 0.0001). The estimated JSWs were deployed to predict the radiographic severity and progression of KOA defined by Kellgren-Lawrence (KL) grades using the XGBoost model. The 64-point multiple-JSWs achieved the best performance in predicting KOA progression within 48 months, with the area-under-receiver operating characteristic curve (AUC) of 0.621, outperforming the commonly used minimum-JSW with 0.554 AUC. We provided a fully automated radiographic assessment tool for KOA with comparable performance to the radiologists and showed that the fine-grained measurement of multiple-JSWs yields superior prediction performance for KOA over the minimum-JSW.

A siamese network with adaptive gated feature fusion for individual knee OA features grades prediction

Grading individual knee osteoarthritis (OA) features is a fine-grained knee OA severity assessment. Existing methods ignore following problems: (1) more accurately located knee joints benefit subsequent grades prediction; (2) they do not consider knee joints' symmetry and semantic information, which help to improve grades prediction performance. To this end, we propose a SE-ResNext50-32x4d-based Siamese network with adaptive gated feature fusion method to simultaneously assess eight tasks. In our method, two cascaded small convolution neural networks are designed to locate more accurate knee joints. Detected knee joints are further cropped and split into left and right patches via their symmetry, which are fed into SE-ResNext50-32x4d-based Siamese network with shared weights, extracting more detailed knee features. The adaptive gated feature fusion method is used to capture richer semantic information for better feature representation here. Meanwhile, knee OA/non-knee OA classification task is added, helping extract richer features. We specially introduce a new evaluation metric (top±1 accuracy) aiming to measure model performance with ambiguous data labels. Our model is evaluated on two public datasets: OAI and MOST datasets, achieving the state-of-the-art results comparing to competing approaches. It has the potential to be a tool to assist clinical decision making.

Diffuse Large B-Cell Lymphoma Discovered following Total Knee Arthroplasty: A Case Report and Literature Review

Total knee arthroplasty is performed routinely for osteoarthritis of the knee joint. We report a case of high-grade diffuse large B-cell lymphoma as an unexpected diagnosis after histopathological examination of unusual-looking synovium during total knee arthroplasty in a 68-year-old patient without any systemic symptoms.

Association of isometric quadriceps strength with stride and knee kinematics during gait in community dwelling adults with normal knee or early radiographic knee osteoarthritis

BACKGROUND

Identifying indicators of early knee osteoarthritis is important for preventing the onset and/or progression of the disease. Although low quadriceps strength and changes in stride and knee kinematics during gait have been suggested as possible indicators, their relevance and relationships have not been fully examined. This study aimed to analyze the association of quadriceps strength with stride and knee kinematics during gait in adults with normal knee or early knee osteoarthritis.

METHODS

A total of 881 knees from 474 community dwelling adults (238 males and 236 females) were included. Radiographic images of the knee in standing position were obtained, and grading of knee osteoarthritis was classified. Isometric quadriceps strength was measured using a force detector device. Three-dimensional knee kinematics during gait was obtained by a motion capture system. Sex-based difference of quadriceps strength, stride and knee kinematics during gait was evaluated by multiple comparison among grades by sex and multiple regression of quadriceps strength was analyzed by stride and knee kinematics during gait.

FINDINGS

Stride length and quadriceps strength were significantly reduced with higher grade in both sexes, and changes in knee kinematics during gait differed by sex from early knee osteoarthritis. Quadriceps strength in both sexes was significantly correlated with changes in stride length and knee kinematics during gait.

INTERPRETATION

Improving quadriceps strength in early knee osteoarthritis was related with maintaining gait ability and restraining abnormal knee kinematics during gait. This may help to develop clinical approaches to prevent the onset and/or progression of knee osteoarthritis.

Computer-Assisted System in Stress Radiography for Anterior Cruciate Ligament Injury with Correspondent Evaluation of Relevant Diagnostic Factors

We sought to design a computer-assisted system measuring the anterior tibial translation in stress radiography, evaluate its diagnostic performance for an anterior cruciate ligament (ACL) tear, and assess factors affecting the diagnostic accuracy. Retrospective research for patients with both knee stress radiography and magnetic resonance imaging (MRI) at our institution was performed. A complete ACL rupture was confirmed on an MRI. The anterior tibial translations with four different methods were measured in 249 patients by the designed algorithm. The diagnostic accuracy of each method in patients with all successful measurements was evaluated. Univariate logistic regression analysis for factors affecting diagnostic accuracy of method four was performed. In the inclusive 249 patients, 177 patients (129 with completely torn ACLs) were available for analysis. Mean anterior tibial translations were significantly increased in the patients with a completely torn ACL by all four methods, with diagnostic accuracies ranging from 66.7% to 75.1%. The diagnostic accuracy of method four was negatively associated with the time interval between stress radiography and MRI as well as force-joint distance on stress view, and not significantly associated with age, gender, flexion angle, intercondylar distance, and force-joint angle. A computer-assisted system measuring the anterior tibial translation in stress radiography showed acceptable diagnostic performance of complete ACL injury. A shorter time interval between stress radiography and MRI as well as shorter force-joint distance were associated with higher diagnostic accuracy.

A novel method for determining the Femoral-Tibial Angle of Knee Osteoarthritis on X-ray radiographs: data from the Osteoarthritis Initiative

Femoral-tibial alignment is a prominent risk factor for Knee Osteoarthritis (KOA) incidence and progression. One way of assessing alignment is by determining the Femoral-Tibial Angle (FTA). Several studies have investigated FTA determination; however, methods of assessment of FTA still present challenges. This paper introduces a new method for semi-automatic measurement of FTA as part of KOA research. Our novel approach combines preprocessing of X-ray images and the use of Active Shape Model (ASM) as the femoral and tibial segmentation method, followed by a thinning process. The result of the thinning process is used to predict FTA automatically by measuring the angle between the intersection of the two vectors of branching points on the femoral and tibial areas. The proposed method is trained on 10 x-ray images and tested on 50 different x-ray images of the Osteoarthritis Initiative (OAI) dataset. The outcomes of this approach were compared with manually obtained FTA measurements from the OAI dataset as the ground truth. Based on experiments, the difference in measurement results between the FTA of the OAI and the FTA obtained using our method is quite small, i.e., below 0.81° for the right FTA and below 0.77° for the left FTA with minimal average errors. This result indicates that this method is clinically suitable for semi-automatic measurement of the FTA.

Automatic Annotation of Narrative Radiology Reports

Narrative texts in electronic health records can be efficiently utilized for building decision support systems in the clinic, only if they are correctly interpreted automatically in accordance with a specified standard. This paper tackles the problem of developing an automated method of labeling free-form radiology reports, as a precursor for building query-capable report databases in hospitals. The analyzed dataset consists of 1295 radiology reports concerning the condition of a knee, retrospectively gathered at the Clinical Hospital Centre Rijeka, Croatia. Reports were manually labeled with one or more labels from a set of 10 most commonly occurring clinical conditions. After primary preprocessing of the texts, two sets of text classification methods were compared: (1) traditional classification models-Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forests (RF)-coupled with Bag-of-Words (BoW) features (i.e., symbolic text representation) and (2) Convolutional Neural Network (CNN) coupled with dense word vectors (i.e., word embeddings as a semantic text representation) as input features. We resorted to nested 10-fold cross-validation to evaluate the performance of competing methods using accuracy, precision, recall, and F 1 score. The CNN with semantic word representations as input yielded the overall best performance, having a micro-averaged F 1 score of 86 . 7 % . The CNN classifier yielded particularly encouraging results for the most represented conditions: degenerative disease ( 95 . 9 % ), arthrosis ( 93 . 3 % ), and injury ( 89 . 2 % ). As a data-hungry deep learning model, the CNN, however, performed notably worse than the competing models on underrepresented classes with fewer training instances such as multicausal disease or metabolic disease. LR, RF, and SVM performed comparably well, with the obtained micro-averaged F 1 scores of 84 . 6 % , 82 . 2 % , and 82 . 1 % , respectively.

Toward automatic quantification of knee osteoarthritis severity using improved Faster R-CNN

PURPOSE

Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Radiologists usually review knee X-ray images and grade the severity of the impairments according to the Kellgren-Lawrence grading scheme. However, this approach becomes inefficient in hospitals with high throughput as it is time-consuming, tedious and also subjective. This paper introduces a model for automatic diagnosis of knee OA based on an end-to-end deep learning method.

METHOD

In order to process the input images with location and classification simultaneously, we use Faster R-CNN as baseline, which consists of region proposal network (RPN) and Fast R-CNN. The RPN is trained to generate region proposals, which contain knee joint and then be used by Fast R-CNN for classification. Due to the localized classification via CNNs, the useless information in X-ray images can be filtered and we can extract clinically relevant features. For the further improvement in the model's performance, we use a novel loss function whose weighting scheme allows us to address the class imbalance. Besides, larger anchors are used to overcome the problem that anchors don't match the object when increasing the input size of X-ray images.

RESULT

The performance of the proposed model is thoroughly assessed using various measures. The results show that our adjusted model outperforms the Faster R-CNN, achieving a mean average precision nearly 0.82 with a sensitivity above 78% and a specificity above 94%. It takes 0.33 s to test each image, which achieves a better trade-off between accuracy and speed.

CONCLUSION

The proposed end-to-end fully automatic model which is computationally efficient has the potential to achieve the real automatic diagnosis of knee OA and be used as computer-aided diagnosis tools in clinical applications.

Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images

Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments according to the Kellgren and Lawrence grading scheme; a five-point ordinal scale (0-4). In this study, we used Elastic Net (EN) and Random Forests (RF) to build predictive models using patient assessment data (i.e. signs and symptoms of both knees and medication use) and a convolution neural network (CNN) trained using X-ray images only. Linear mixed effect models (LMM) were used to model the within subject correlation between the two knees. The root mean squared error for the CNN, EN, and RF models was 0.77, 0.97 and 0.94 respectively. The LMM shows similar overall prediction accuracy as the EN regression but correctly accounted for the hierarchical structure of the data resulting in more reliable inference. Useful explanatory variables were identified that could be used for patient monitoring before X-ray imaging. Our analyses suggest that the models trained for predicting the KOA severity levels achieve comparable results when modeling X-ray images and patient data. The subjectivity in the KL grade is still a primary concern.

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

BACKGROUND

Osteoarthritis (OA) is the most common disease of arthritis. Analgesics are widely used in the treat of arthritis, which may increase the risk of cardiovascular diseases by 20% to 50% overall.There are few studies on the side effects of OA medication, especially the risk prediction models on side effects of analgesics. In addition, most prediction models do not provide clinically useful interpretable rules to explain the reasoning process behind their predictions. In order to assist OA patients, we use the eXtreme Gradient Boosting (XGBoost) method to balance the accuracy and interpretability of the prediction model.

RESULTS

In this study we used the XGBoost model as a classifier, which is a supervised machine learning method and can predict side effects of analgesics for OA patients and identify high-risk features (RFs) of cardiovascular diseases caused by analgesics. The Electronic Medical Records (EMRs), which were derived from public knee OA studies, were used to train the model. The performance of the XGBoost model is superior to four well-known machine learning algorithms and identifies the risk features from the biomedical literature. In addition the model can provide decision support for using analgesics in OA patients.

CONCLUSION

Compared with other machine learning methods, we used XGBoost method to predict side effects of analgesics for OA patients from EMRs, and selected the individual informative RFs. The model has good predictability and interpretability, this is valuable for both medical researchers and patients.

Three-dimensional mapping of the joint space for the diagnosis of knee osteoarthritis based on high resolution computed tomography: Comparison with radiographic, outerbridge, and meniscal classifications

One of the most important characteristic of knee osteoarthritis (OA) is the joint space (JS) width narrowing. Measurements are usually performed on two dimensional (2D) X-rays. We propose and validate a new method to assess the 3D joint space at the medial knee compartment using high resolution peripheral computed tomography images. A semi-automated method was developed to obtain a distance 3D map between femur an tibia with the following parameters: volume, minimum, maximum, mean, standard deviation, median, asymmetry, and entropy. We analyzed 71 knee specimens (mean age: 85 years), radiographs were performed for the Kellgren Lawrence (KL) score grading. In a subgroup of 41 specimens, the histopathological Outerbridge and meniscal classifications were performed and then cores were harvested from the tibial plateau in three different positions (posterior, central, and peripheral) and imaged at 10 µm of resolution to measure the cartilage thickness. Minimum, maximum, mean, and median were statistically lower and entropy higher between knee specimens classified as KL = 0 and KL = 3-4. Gr1 and 2 were statistically different from Gr3-4 for minimum, asymmetry, entropy using the Outerbridge classification and Gr1 was statistically different from Gr3-4 using the meniscal classification. Asymmetry, minimum, mean, median and entropy were significantly correlated with cartilage thickness. Parameters extracted from a 3D map of the medial joint space indicate local variations of JS and are related to local measurements of tibial cartilage thickness, and could be consequently useful to identify early OA. © 2018 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 36:2380-2391, 2018.

Cut-Off Value of Medial Meniscal Extrusion for Knee Pain

Purpose. Medial meniscal extrusion (MME) has attracted attention as an index of knee pain in conjunction with clinical symptoms that could be more useful than the diagnosis of knee osteoarthritis on X-ray. However, the size of MME that would cause knee pain has not been clarified. The aim of the present study was to investigate the cut-off value of MME for knee pain. Methods. A total of 318 knees were evaluated. The presence of current or past knee pain was confirmed by interview. Next, MME was measured using vertical sonographic images of the medial joint spaces during weightbearing. Results. Overall, 71 knees were painful (P-group), and 247 knees were not (N-group). MME was 5.9 ± 1.8 mm in the P-group and 2.9 ± 1.5 mm in the N-group (P < 0.0001). Analysis of the receiver operating characteristic curve showed that the cut-off value of MME for knee pain was 4.3 mm, with sensitivity of 0.8451 and specificity of 0.8502. In addition, 64% of knees without pain cases at the time of examination whose MME exceeded this cut-off value had past knee pain. Conclusions. The sensitivity and specificity of MME for knee pain were very high with a cut-off value of 4.3 mm.

Computer-assisted Joint Space Area Measurement: A New Technique in Patients With Knee Osteoarthritis

Objectives

This study aims to assess the validity and reproducibility of computer-assisted joint space area measurement in knee roentgenograms of patients with knee osteoarthritis and compare it with a qualitative method in knee roentgenograms and quantitative and semi-quantitative methods in magnetic resonance imaging.

Patients and methods

The study included 40 knees of 40 patients diagnosed as osteoarthritis (14 males, 26 females; mean age 57.4±5.9 years; range 47 to 67 years). Only the patients who wrote consents for publication of their radiologic data, and with knee roentgenograms and magnetic resonance images of the same knees were selected. Computer-assisted measurements were applied to joint spaces by two blinded physicians, for two times with an interval of one week. Data were evaluated for intraobserver and interobserver consistency. Also, data were compared with qualitative (Kellgren-Lawrence classification), quantitative (joint space width, cartilage thickness, meniscal thickness in magnetic resonance images) and semi-quantitative methods (whole-organ magnetic resonance imaging score).

Results

Intraobserver consistency was evaluated for each physician, which revealed no differences. Interobserver consistency was evaluated by comparing the measurements of two blinded physicians and no differences were found (p>0.05). There was no significant correlation between the grade of Kellgren-Lawrence classification and other variables; such as grade of meniscus, meniscal thickness, cartilage thickness and computer- assisted joint space area measurements (p>0.05). While there was a positive correlation between computer-assisted joint space area measurement and other quantitative measurements, there was a negative correlation between computer-assisted joint space area measurement and whole-organ magnetic resonance imaging scores.

Conclusion

When compared with qualitative, quantitative, and semi-quantitative methods, computer-assisted joint space area measurement seems to be a useful, reproducible, and cost-effective quantitative method for evaluating knee osteoarthritis.

Semi-automated detection of anterior cruciate ligament injury from MRI

BACKGROUND AND OBJECTIVES

A radiologist's work in detecting various injuries or pathologies from radiological scans can be tiresome, time consuming and prone to errors. The field of computer-aided diagnosis aims to reduce these factors by introducing a level of automation in the process. In this paper, we deal with the problem of detecting the presence of anterior cruciate ligament (ACL) injury in a human knee. We examine the possibility of aiding the diagnosis process by building a decision-support model for detecting the presence of milder ACL injuries (not requiring operative treatment) and complete ACL ruptures (requiring operative treatment) from sagittal plane magnetic resonance (MR) volumes of human knees.

METHODS

Histogram of oriented gradient (HOG) descriptors and gist descriptors are extracted from manually selected rectangular regions of interest enveloping the wider cruciate ligament area. Performance of two machine-learning models is explored, coupled with both feature extraction methods: support vector machine (SVM) and random forests model. Model generalisation properties were determined by performing multiple iterations of stratified 10-fold cross validation whilst observing the area under the curve (AUC) score.

RESULTS

Sagittal plane knee joint MR data was retrospectively gathered at the Clinical Hospital Centre Rijeka, Croatia, from 2007 until 2014. Type of ACL injury was established in a double-blind fashion by comparing the retrospectively set diagnosis against the prospective opinion of another radiologist. After clean up, the resulting dataset consisted of 917 usable labelled exam sequences of left or right knees. Experimental results suggest that a linear-kernel SVM learned from HOG descriptors has the best generalisation properties among the experimental models compared, having an area under the curve of 0.894 for the injury-detection problem and 0.943 for the complete-rupture-detection problem.

CONCLUSIONS

Although the problem of performing semi-automated ACL-injury diagnosis by observing knee-joint MR volumes alone is a difficult one, experimental results suggest potential clinical application of computer-aided decision making, both for detecting milder injuries and detecting complete ruptures.

Absence of a relationship between joint space narrowing and osteophyte formation in early knee osteoarthritis among Japanese community-dwelling elderly individuals: A cross-sectional study

OBJECTIVES

Joint space narrowing and osteophyte formation, radiographic features of knee osteoarthritis (OA), are not necessarily synchronous processes. We evaluated the relationship between medial minimum joint space width (mJSW) and osteophyte formation.

METHODS

We conducted a retrospective study of 1050 individuals (424 males; 626 females; mean age 64.9 years) who underwent knee radiography as part of a health screening program, between 2011 and 2013. mJSW and tibial osteophyte area (OF) were quantified using automated software. The mJSW range was subdivided into tertiles, and OF, mJSW, and quality of life (QOL) were compared among them. Correlation between OF and mJSW was evaluated.

RESULTS

In females, OF was largest and correlated with mJSW only in the lowest tertile group. Patients in the lowest mJSW tertile group had a lower QOL and higher pain than those in the other two groups. Based on our generalized additive models and a receiver operating characteristic curve analysis, an mJSW cutoff point of 3.5 mm was apparent in females, with no significant cutoff identified in males.

CONCLUSIONS

OF correlates with mJSW below a cutoff value of about 3.5 mm in females. OA symptoms, namely physical function impairment and pain, increases significantly as mJSW decreases below the cutoff. .

Laterality of radiographic osteoarthritis of the knee

There are few reports of the laterality in radiological knee osteoarthritis (ROA). This study aimed to evaluate laterality in terms of the minimum joint space width (mJSW) and osteophyte areas (OFs) in a cross-sectorial general population screen and elucidate the association between laterality and risk of osteoarthritis. We enrolled 330 participants (mean age 64.6 years) and examined the presence of ROA (Kellgren-Lawrence grade ≧ 2) laterality in terms of the mJSW and OF on the medial tibia using auto-measuring software. Moreover, we examined the association between laterality and leg dominance. The right and left medial mJSWs were 4.02 ± 0.98 mm and 4.05 ± 1.01 mm, respectively, showing no laterality; the laterals were also similar. The participants who had osteophytes ≥1 mm² in the right, left, and bilateral knees were 15, 37, and 57 respectively, with osteophytes being significantly more common in the left knee. The OF was significantly larger in the left knee. Conversely, the medial and lateral mJSWs and OF did not differ according to leg dominance. The prevalence of ROA was higher and the OF was more pronounced in the left knee. However, the mJSW showed no laterality. Additionally, the mJSW and OF showed no differences according to leg dominance.

Locomotive Syndrome: Operational Definition Based on a Questionnaire, and Exercise Interventions on Mobility Dysfunction in Elderly People

The increasing elderly population has a great impact on public health, and it is important to understand the progression of musculoskeletal disorders seen in this population. To establish useful preventative methods for such locomotive disorders, we must detect early changes in these individuals and identify those at risk in order to implement early interventions. The purpose of this review was: (1) to introduce an operational definition of locomotion dysfunction to prevent a care-need condition, and to verify its validity through a prospective cohort study, and (2) to review the indication of exercise intervention for multiple musculoskeletal involvements from the preceding literature. We developed a measurement scale called the Geriatric Locomotive Function Scale (GLFS)-25, which clearly reflects the degree of functional deterioration. We used it in a prospective cohort study of 314 patients recruited from 5 clinics or nursing care facilities and investigated the relationship of the GLFS-25 with 46 variables covering various clinical manifestations. The results clearly revealed that the change in the GLFS-25 classification reflected a common pattern seen in those with locomotive dysfunction. Recently, several important movements regarding physical activity and its public promotion have been advocated by international health organizations and journal publishers. Though it has not been confirmed yet that complex musculoskeletal diseases can be treated using therapeutic exercise, the promotion of physical activity appears promising. The degree of activity limitation in aged individuals with locomotive disorders can be evaluated using this scale, which may be useful in predicting the effectiveness of future interventions.

Early osteoarthritis of the knee

There is an increasing awareness on the importance in identifying early phases of the degenerative processes in knee osteoarthritis (OA), the crucial period of the disease when there might still be the possibility to initiate treatments preventing its progression. Early OA may show a diffuse and ill-defined involvement, but also originate in the cartilage surrounding a focal lesion, thus necessitating a separate assessment of these two entities. Early OA can be considered to include a maximal involvement of 50 % of the cartilage thickness based on the macroscopic ICRS classification, reflecting an OARSI grade 4. The purpose of this paper was to provide an updated review of the current status of the diagnosis and definition of early knee OA, including the clinical, radiographical, histological, MRI, and arthroscopic definitions and biomarkers. Based on current evidence, practical classification criteria are presented. As new insights and technologies become available, they will further evolve to better define and treat early knee OA.

Validity of radiographic assessment of the knee joint space using automatic image analysis

OBJECTIVES

The present study investigated whether there were differences between automatic and manual measurements of the minimum joint space width (mJSW) on knee radiographs.

METHODS

Knee radiographs of 324 participants in a systematic health screening were analyzed using the following three methods: manual measurement of film-based radiographs (Manual), manual measurement of digitized radiographs (Digital), and automatic measurement of digitized radiographs (Auto). The mean mJSWs on the medial and lateral sides of the knees were determined using each method, and measurement reliability was evaluated using intra-class correlation coefficients. Measurement errors were compared between normal knees and knees with radiographic osteoarthritis.

RESULTS

All three methods demonstrated good reliability, although the reliability was slightly lower with the Manual method than with the other methods. On the medial and lateral sides of the knees, the mJSWs were the largest in the Manual method and the smallest in the Auto method. The measurement errors of each method were significantly larger for normal knees than for radiographic osteoarthritis knees.

CONCLUSIONS

The mJSW measurements are more accurate and reliable with the Auto method than with the Manual or Digital method, especially for normal knees. Therefore, the Auto method is ideal for the assessment of the knee joint space.

Relationship between Pain and Medial Meniscal Extrusion in Knee Osteoarthritis

Purpose. In knee osteoarthritis, the degree of pain varies despite similar imaging findings. If there were quantitative findings related to the pain of knee osteoarthritis, it could be used for diagnosis or screening. The medial meniscal extrusion was investigated as a candidate quantitative finding related to the pain of knee osteoarthritis. Methods. Seventy-six knees of 38 patients (mean age, 73 years) who received intra-articular injections of hyaluronic acid into unilateral knees at the time of diagnosis of knee arthritis were investigated. Cartilage thickness of the femoral medial condyle and medial meniscal extrusion of bilateral knees were measured by ultrasonography. Thirty-eight knees that had hyaluronic acid injections were compared with 38 other side knees from the same patients as the control group. Results. The average cartilage thicknesses of the knees with pain that received intra-articular injections and the knees without pain that received no injections were 1.02 and 1.05 mm, respectively (P = 0.6394). On the other hand, the average medial meniscal extrusions of the knees with and without pain were 7.58 and 5.88 mm, respectively (P = 0.0005); pain was associated with greater medial meniscal extrusions. Conclusion. Medial meniscal extrusion is a quantitative finding related to the pain of knee osteoarthritis.

Serum hyaluronic acid concentration predicts the progression of joint space narrowing in normal knees and established knee osteoarthritis - a five-year prospective cohort study

Introduction

Serum hyaluronic acid (sHA) is a serum biomarker for knee osteoarthritis (OA). Although sHA concentration is elevated in patients with knee OA, the relationship between serum concentration and disease progression remains unclear. We examined the relationship between sHA concentration and radiographic progression of knee OA in a cohort of individuals followed for 5 years.

Methods

We prospectively enrolled 444 individuals and measured their sHA concentrations at baseline. Anterior-posterior weight bearing knee radiographs were obtained at baseline and the 5-year endpoint. Osteoarthritic knee changes were classified according to Kellgren–Lawrence (KL) grade, and joint space narrowing (JSN) was measured using a Knee Osteoarthritis Computer-Aided Diagnosis (KOACAD) system. Correlations between sHA concentration, progression in KL grade, and JSN were assessed using regression models, taking into account potentially confounding factors.

Results

OA progressed from KL grades 0 or 1 in 129 of the 323 knees, and from KL grades 2 or 3 in 61 of the 119 knees. Higher sHA concentrations were correlated with KL grade progression (p = 0.004). The mean JSN, as assessed by KOACAD over 5 years, was 0.23 ± 0.55 mm, and sHA concentration was positively correlated with progression of JSN in KL grades 0 or 1 (p = 0.021) and KL grades 2 or 3 (p = 0.008) knees.

Conclusion

Serum HA concentration was positively correlated with progression of KL grade. sHA was also positively correlated with progression of JSN in knees with and without OA, suggesting that sHA concentration may be a useful predictor of knee OA progression.

Radiographic assessment of leg alignment and grading of knee osteoarthritis: A critical review

Knee osteoarthritis (OA) is a progressive joint disease hallmarked by cartilage and bone breakdown and associated with changes to all of the tissues in the joint, ultimately causing pain, stiffness, deformity and disability in many people. Radiographs are commonly used for the clinical assessment of knee OA incidence and progression, and to assess for risk factors. One risk factor for the incidence and progression of knee OA is malalignment of the lower extremities (LE). The hip-knee-ankle (HKA) angle, assessed from a full-length LE radiograph, is ideally used to assess LE alignment. Careful attention to LE positioning is necessary to obtain the most accurate measurement of the HKA angle. Since full-length LE radiographs are not always available, the femoral shaft - tibial shaft (FS-TS) angle may be calculated from a knee radiograph instead. However, the FS-TS angle is more variable than the HKA angle and it should be used with caution. Knee radiographs are used to assess the severity of knee OA and its progression. There are three types of ordinal grading scales for knee OA: global, composite and individual feature scales. Each grade on a global scale describes one or more features of knee OA. The entire description must be met for a specific grade to be assigned. The Kellgren-Lawrence scale is the most commonly-used global scale. Composite scales grade several features of knee OA individually and sum the grades to create a total score. One example is the compartmental grading scale for knee OA. Composite scales can respond to change in a variety of presentations of knee OA. Individual feature scales assess one or more OA features individually and do not calculate a total score. They are most often used to monitor change in one OA feature, commonly joint space narrowing. The most commonly-used individual feature scale is the OA Research Society International atlas. Each type of scale has its advantages; however, composite scales may offer greater content validity. Responsiveness to change is unknown for most scales and deserves further evaluation.

Joint space narrowing, body mass index, and knee pain: the ROAD study (OAC1839R1)

OBJECTIVE

The objective of the present study was to clarify the association of joint space narrowing with knee pain in Japanese men and women using a large-scale population-based cohort of the Research on Osteoarthritis/osteoporosis Against Disability (ROAD) study.

METHODS

This study examined the association between minimum joint space width (mJSW) in the medial compartment and pain at the knee. mJSW was measured in the medial and lateral compartments of the knee using a knee osteoarthritis (OA) computer-aided diagnosis system.

RESULTS

From the 3040 participants in the ROAD study, the present study analyzed 2733 participants who completed the radiographic examinations and questionnaires regarding knee pain (975 men and 1758 women; mean age, 69.9 ± 11.2 years). Subjects with lateral knee OA were excluded. After adjustment for age and Body mass index (BMI), medial mJSW, as well as medial mJSW/lateral mJSW, was significantly associated with knee pain. Sex and BMI affected the association of medial mJSW with knee pain. The threshold of medial mJSW was approximately 3 mm in men and 2 mm in women, while that of medial mJSW/lateral mJSW was approximately 60% in both men and women. BMI was found to have a distinct effect on the association of mJSW with pain.

CONCLUSION

The present cross-sectional study using a large-scale population from the ROAD study showed that joint space narrowing had a significant association with knee pain. The thresholds of joint space narrowing for knee pain were also established.

Serum levels of 25-hydroxyvitamin D and the occurrence of musculoskeletal diseases: a 3-year follow-up to the road study

Assessment of serum 25-hydroxyvitamin D levels in association with the occurrence of musculoskeletal diseases using a population-based cohort study design revealed that serum 25-hydroxyvitamin D levels could predict the occurrence of osteoporosis at the femoral neck within 3 years, but not the occurrence of knee osteoarthritis or lumbar spondylosis.

INTRODUCTION

The aim of this study is to clarify the association between serum 25-hydroxyvitamin D (25D) levels and occurrence of osteoporosis and osteoarthritis in the general population.

METHODS

The Research on Osteoarthritis/Osteoporosis Against Disability study, a large-scale population-based cohort study, was performed during 2005-2007. Serum 25D levels were measured in 1,683 participants. Of these, 1,384 individuals (81.9%) completed a second follow-up survey 3 years later. Osteoporosis was defined according to World Health Organization criteria, in which osteoporosis is diagnosed by T-scores of bone mineral density (BMD) that are 2.5 standard deviations (SD) less than normal BMD. Knee osteoarthritis and lumbar spondylosis were defined as Kellgren-Lawrence grade ≥2, using paired X-ray films. Cumulative incidences were determined according to changes in measurements using World Health Organization criteria for osteoporosis or Kellgren-Lawrence grades for osteoarthritis between the baseline and second survey.

RESULTS

The mean (SD) serum 25D level of the 1,384 participants in both surveys was 23.4 ng/mL (6.5). The annual cumulative incidences of osteoporosis at L2-4 and the femoral neck were 0.76 and 1.83%/year, respectively. The incidences of knee osteoarthritis and lumbar spondylosis were 3.3 and 11.4%/year, respectively. After adjusting for potential associated factors, logistic regression analyses revealed that the odds ratio for the occurrence of femoral neck osteoporosis significantly decreased as serum 25D levels increased (+1 SD; odds ratio 0.67; 95% confidence interval 0.49-0.92; p = 0.014).

CONCLUSIONS

Higher serum 25D levels may prevent the occurrence of osteoporosis at the femoral neck, but not knee osteoarthritis, lumbar spondylosis, or osteoporosis at L2-4.

Evaluation of the relationship between T1ρ and T2 values and patella cartilage degeneration in patients of the same age group

OBJECTIVE

The aim of this study was to investigate the association between the T1ρ and T2 values and the progression of cartilage degeneration in patients of the same age group.

MATERIALS AND METHODS

Sagittal T1ρ and T2 mapping and three-dimensional (3D) gradient-echo images were obtained from 78 subjects with medial knee osteoarthritis (OA). The degree of patella cartilage degeneration was classified into four groups using MRI-based grading: apparently normal cartilage, mild OA, moderate OA, and severe OA group. We measured the T1ρ and T2 values (ms) in the regions of interest set on the full-thickness patella cartilage. Then, we analyzed the relationship between the T1ρ and T2 values and the degree of patella cartilage degeneration.

RESULTS

There were no significant differences in age among the four groups. Both the T1ρ and T2 values showed a positive correlation with the degree of OA progression (ρ=0.737 and ρ=0.632, respectively). By comparison between the apparently normal cartilage and the mild OA groups, there were significant differences in the T1ρ mapping, but not in the T2 mapping.

CONCLUSIONS

Our study confirmed that T1ρ and T2 mapping can quantitatively evaluate the degree of patella cartilage degeneration in patients within the same age group.

Association of dietary intake with joint space narrowing and osteophytosis at the knee in Japanese men and women: the ROAD study

OBJECTIVE

The objective of the present study is to identify dietary nutrients associated with joint space narrowing (JSN) and osteophytosis at the knee in a population-based cohort of the Research on Osteoarthritis/osteoporosis Against Disability (ROAD) study.

METHODS

From the baseline survey of the ROAD study, 827 participants (305 men and 522 women) in a rural cohort were analyzed. Dietary nutrient intakes for the last month were assessed by a self-administered brief diet history questionnaire. Minimum joint space width (mJSW) and osteophyte area (OPA) in the medial compartment of the knee were measured using a knee osteoarthritis (OA) computer-aided diagnostic system.

RESULTS

In men, there were no associations of dietary nutrient intakes with mJSW or OPA. In women, vitamins K, B1, B2, B6, and C were associated with mJSW after adjustment for age, body mass index, and total energy (p < 0.05). Vitamins E, K, B1, B2, niacin, and B6 were significantly associated with OPA (p < 0.05) in women. Vitamins K, B and C may have a protective role against knee OA in women and might lead to disease-modifying treatments.

CONCLUSIONS

The present study revealed that low dietary intake of vitamins K, B, and C are associated with JSN and osteophytosis in women.

Does osteophytosis at the knee predict health-related quality of life decline? A 3-year follow-up of the ROAD study

The objective of the present longitudinal study was to clarify whether osteophytosis and joint space narrowing predict quality of life (QOL) decline using a longitudinal population-based cohort of the Research on Osteoarthritis/osteoporosis Against Disability (ROAD) study. The present study analyzed 1,525 participants who completed the radiographic examination at baseline and questionnaires regarding QOL at a 3-year follow-up (546 men and 979 women; mean age, 67.0 ± 11.0 years). This study examined the associations of osteophyte area (OPA) and minimum joint space width (mJSW) in the medial compartment of the knee at baseline with pain and physical functional disability measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). OPA and mJSW in the medial compartment of the knee were measured using a knee osteoarthritis (OA) computer-aided diagnosis system. Overall, OPA independently predicted physical functional disability after 3 years of follow-up. When analyzed in men and women separately, OPA, rather than mJSW, was an independent predictor for pain and physical functional disability after 3 years of follow-up in men. OPA, rather than mJSW, also predicted worsening of pain in men during the 3-year follow-up, whereas in women, mJSW, rather than OPA, predicted worsening of pain. In conclusion, the present longitudinal study using a large-scale population from the ROAD study found gender differences in the association of osteophytosis and joint space narrowing with pain and physical functional disability.