West Lake Staging: A New Staging System Orchestrated by X-Ray and MRI on Knee Osteoarthritis

2020 ◽  
Author(s):  
Jiaxin Huang ◽  
Xi Chen ◽  
Mengting Xia ◽  
Shuaijie Lv ◽  
Peijian Tong
Keyword(s):  
Knee Osteoarthritis ◽  
Cartilage Defect ◽  
Staging System ◽  
Joint Space ◽  
Youden Index ◽  
X Rays ◽  
Full Thickness ◽  
West Lake ◽  
X Ray ◽  
Orthopedic Surgeons

Abstract Background: Although widely used of Kellgren - Lawrence (KL) staging system on knee osteoarthritis (KOA), it still had some disadvantages. The purpose of this study was to investigate the differences on X-ray and MRI among each stage of KOA and further propose a new staging system called West Lake (WL) staging.Methods: A cross-sectional study was conducted on patients with KOA. KL stages were defined by two qualified orthopedic surgeons. Joint space widths (JSW) were measured on X-rays, whereas cartilage injuries (CI) and bone marrow lesions (BML) were evaluated on MRI. The differences of them across the groups were calculated by T-test. Receiver operating characteristic (ROC) curves were rendered to obtain the areas under the curves (AUC), Youden index and corresponding cut-off points. Agreement Index (KAPPA) was used to analyze the consistency of KL stages and WL stages assessed by two qualified orthopedic surgeons. Results: Eventually, a total of 220 patients met the criteria. There were significant differences on JSW, CI and BML between KL II/III as well as KL III/IV. In KL II/III, the AUC of JSW, CI, BML was 0.99, 0.76, 0.71 and the Youden index was 0.94, 0.38, 0.45, meanwhile the cut-off points were £5.1mm, >1, >2. In KL III/IV, the AUC of JSW, CI, BML was 0.96, 0.79, 0.74 and the Youden index was 0.84, 0.58, 0.38, meanwhile the cut-off points were £3.2mm, >3, >4. The Kappa coefficients of KL stages and WL stages were 0.31 and 0.76.Conclusion: The WL staging was described as follows: Grade 0, normal X-ray. Grade 1, X-ray shows osteophyte but no joint space narrow, normal MRI or MRI shows cartilage degeneration and only 1 or 2 sections are involved in BML. Grade 2, X-ray shows joint space narrow, MRI shows cartilage defect but no full-thickness cartilage defect, meanwhile 3 or 4 sections are involved in BML. Grade 3, X-ray shows serious joint space narrow even JSW disappeared, MRI shows full-thickness cartilage defect, more than 4 sections are involved in BML.Trial registration: The study was approved by the First Affiliated Hospital of Zhejiang Chinese Medical University (2018-ZX-026-01).

scholarly journals West Lake staging: A new staging system orchestrated by X-ray and MRI on knee osteoarthritis

2021 ◽  
Vol 29 (3) ◽  
pp. 230949902110495
Author(s):  
Jiaxin Huang ◽  
Xi Chen ◽  
Mengting Xia ◽  
Shuaijie Lv ◽  
Peijian Tong
Keyword(s):  
Knee Osteoarthritis ◽  
Cartilage Defect ◽  
Staging System ◽  
Joint Space ◽  
Stage I ◽  
Stage Ii ◽  
Youden Index ◽  
Full Thickness ◽  
West Lake ◽  
X Ray

Purpose: To investigate the differences on X-ray and MRI among each stage of knee osteoarthritis (KOA) and further propose a new staging system called West Lake (WL) staging. Methods: A cross-sectional study was conducted on patients with KOA. Stage I, II, III, and IV were divided based on stepwise treatment strategy of Knee osteoarthritis (KOA). Joint space widths (JSW) were measured on X-rays, whereas cartilage injuries (CI) and bone marrow lesions (BML) were evaluated on MRI. The differences of them across the groups were calculated by T-test. Receiver operating characteristic (ROC) curves were rendered to obtain the areas under the curves (AUC), Youden index and corresponding cut-off points. Results: Eventually, there were significant differences on JSW, CI, and BML between stage II/III and III/IV, while no significant differences between stage I/II. In stage II/III, the AUC of JSW, CI, BML was 0.99, 0.76, 0.71 and the Youden index was 0.94, 0.38, 0.45, meanwhile the cut-off points were ≤5.1 mm, >1, >2. In stage III/IV, the AUC of JSW, CI, BML was 0.96, 0.79, 0.74 and the Youden index was 0.84, 0.58, 0.38, meanwhile the cut-off points were ≤3.2 mm, >3, >4. Conclusion: The WL staging was described as follows: Stage I, X-ray shows no joint space narrow, normal MRI or MRI shows cartilage degeneration and only 1 or 2 sections are involved in BML. Stage II, X-ray shows joint space narrow, MRI shows cartilage defect but no full-thickness cartilage defect, meanwhile 3 or 4 sections are involved in BML. Stage III, X-ray shows serious joint space narrow even JSW disappeared, MRI shows full-thickness cartilage defect, more than 4 sections are involved in BML.

Convolutional Neural Network-Based Automatic Measurement of Joint Space Width to Predict Radiographic Severity and Progression of Knee Osteoarthritis

2021 ◽  
Author(s):  
James Chung Wai Cheung ◽  
Yiu Chow TAM ◽  
Lok Chun CHAN ◽  
Ping Keung CHAN ◽  
Chunyi WEN
Keyword(s):  
Neural Network ◽  
Knee Osteoarthritis ◽  
Convolutional Neural Network ◽  
Pearson Correlation ◽  
Joint Space Width ◽  
Joint Space ◽  
Receiver Operating Curve ◽  
Radiographic Severity ◽  
X Ray ◽  
Space Width

Abstract Objectives To develop a deep convolutional neural network (CNN) for the segmentation of femur and tibia on plain x-ray radiographs, hence enabling an automated measurement of joint space width (JSW) to predict the severity and progression of knee osteoarthritis (KOA). Methods A CNN with ResU-Net architecture was developed for knee X-ray imaging segmentation. The efficiency was evaluated by the Intersection over Union (IoU) score by comparing the outputs with the annotated contour of the distal femur and proximal tibia. By leveraging imaging segmentation, the minimal and multiple JSWs in the tibiofemoral joint were estimated and then validated by radiologists’ measurements in the Osteoarthritis Initiative (OAI) dataset using Pearson correlation and Bland–Altman plot. 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 classification performance was assessed using F1 and area under receiver operating curve (AUC). Results The network has attained a segmentation efficiency of 98.9% IoU. Meanwhile, the agreement between the CNN-based estimation and radiologist’s measurement of minimal JSW reached 0.7801 (p < 0.0001). Moreover, the 32-point multiple JSW obtained the highest AUC score of 0.656 to classify KL-grade of KOA. Whereas the 64-point multiple JSWs achieved the best performance in predicting KOA progression defined by KL grade change within 48 months, with AUC of 0.621. The multiple JSWs outperform the commonly used minimum JSW with 0.587 AUC in KL-grade classification and 0.554 AUC in disease progression prediction. Conclusion Fine-grained characterization of joint space width of KOA yields comparable performance to the radiologist in assessing disease severity and progression. We provide a fully automated and efficient radiographic assessment tool for KOA.

Accurate joint space quantification in knee osteoarthritis: a digital x-ray tomosynthesis phantom study

2011 ◽  
Author(s):  
Tanzania S. Sewell ◽  
Kelly L. Piacsek ◽  
Beth A. Heckel ◽  
John M. Sabol
Keyword(s):  
Knee Osteoarthritis ◽  
Joint Space ◽  
Phantom Study ◽  
X Ray

scholarly journals Normal And Abnormal Detection For Knee Osteoarthritis Using Machine Learning Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 6026-6033 ◽  
Keyword(s):  
Machine Learning ◽  
Knee Joint ◽  
Imaging Techniques ◽  
Joint Space ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Abnormal Detection ◽  
X Ray ◽  
Knee Oa ◽  
Computer Aided

Osteoarthritis is the most broadly recognized disease in the knee joint that affects the cartilage, especially among the old age or overweight people. In the normal knee joint, the smooth and thin layer called cartilage covers the joint space of the bone and makes the joint smooth and prevents them from rubbing against one another, but can break, when the cartilage gets ruptured due to which bones start rubbing with one another, and this may cause severe pain, swelling and stiffness in the knee joint. The evaluation for osteoarthritis detection includes a clinical examination, and different medical imaging techniques are X-RAY images and MRI scans. There is developing method required for classification frameworks that can precisely distinguish and identify knee OA from plain radiographs. In this method we have examining the strategy of computer aided diagnosis for early identification of knee OA. Based on the procedure of x rays through computer image processing, segmentation, feature extraction and investigation by means of building a classifier, a viable computer aided detection method for knee was made to help specialists in their precise, convenient and identification of potential risk of OA. For this method a total of 126 knee x ray image were collected for assessing the knee OA. In this paper, we tried to diagnose about the normal or abnormal detection of cartilage depreciation. The HOG and DWT features are extracted from X-ray images of the knee joints. The extracted features are classified with two different machine learning classifiers, namely the SVM and ANN Patternet classifiers, and the results are demonstrated. The SVM classification is good when compared with ANN and provides a satisfactory accuracy rate of 85.33%. At last the classifier was superior both in time effectiveness and classification execution to the regularly utilized classifiers based on iterative learning. In this way it was suitable to utilize as a computer aided tool for the diagnosis of OA.

Imaging the Earth’s magnetic environment in soft X-rays with SMILE

2020 ◽  
Author(s):  
Graziella Branduardi-Raymont ◽  
Steve Sembay ◽  
Tianran Sun ◽  
Hyunju Connor ◽  
Andrey Samsonov
Keyword(s):  
Solar Wind ◽  
Joint Space ◽  
Space Mission ◽  
Standoff Distance ◽  
X Rays ◽  
X Ray ◽  
The Earth ◽  
X Ray Imaging ◽  
Chinese Academy Of Sciences ◽  
Academy Of Sciences

&lt;p&gt;It is a relatively recent discovery that charge exchange soft X-ray emission is produced in the interaction of solar wind high charge ions with neutrals in the Earth&amp;#8217;s exosphere; this has led to the realization that imaging this emission will provide us with a global and novel way to study solar-terrestrial interactions.&lt;/p&gt;&lt;p&gt;In particular X-ray imaging will provide us with the means of establishing the location of the magnetopause and the morphology of the magnetospheric cusps. Variations of the magnetopause standoff distance indicate global magnetospheric compressions and expansions, both in response to solar wind variations and internal magnetospheric processes.&lt;/p&gt;&lt;p&gt;Soft X-ray imaging is one of the main objectives of SMILE (Solar wind Magnetosphere Ionosphere Link Explorer), a joint space mission by ESA and the Chinese Academy of Sciences, which is under development and is due for launch in 2023. This presentation will introduce the scientific aims of SMILE, show simulations of the expected images to be returned by SMILE&amp;#8217;s Soft X-ray Imager for different solar wind conditions, and will discuss some of the techniques that will be applied in order to extract the positions of the Earth&amp;#8217;s magnetic boundaries, such as the magnetopause standoff distance.&lt;/p&gt;

scholarly journals Assessment of safety and efficacy of Karallief® Easy ClimbTM, an herbal extract blend for supporting joint health: a double-blind, placebo-controlled, randomized clinical trial

2020 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Krishna Rajendran ◽  
Karen F. Vieira ◽  
Rajendran Ramaswamy ◽  
Robert M. Koffie ◽  
Ravikumar Rajendran ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Knee Flexion ◽  
Joint Space ◽  
Herbal Extract ◽  
Controlled Study ◽  
X Rays ◽  
Double Blind ◽  
Chair Stand ◽  
Stand Test ◽  
Chair Stand Test

Background: Osteoarthritis is common among the aging population worldwide. The current techniques to manage osteoarthritis focus on relieving pain and slowing the progression of the disease. Herbal or natural supplements have shown promise in achieving both these treatment goals. Two new proprietary herbal extract blends, Karallief® Easy ClimbTM (KEC) and herbal extracts with glucosamine (HEG), are combinations of several natural products shown to be effective in the treatment of knee osteoarthritis. The current study tested the efficacy and safety of KEC and HEG versus a placebo control.Methods: This is a randomized, double-blind and placebo-controlled study. A total of 120 patients were divided into 3 groups and were given KEC, HEG and Placebo in the ratio 1:1:1. Treatment results were assessed using the 30 second chair stand test, WOMAC test, knee flexion test and joint space measurement using X-rays of the knee joint.Results: The study found that the herbal supplements HEG and KEC significantly reduced osteoarthritis-related knee pain and increased joint mobility and were safe to use during 120 days of treatment. Both supplements resulted in an improvement in the 30 second chair stand test results, WOMAC pain scores, knee flexion, and joint space width as measured by X-ray, as compared to the placebo.Conclusions: Natural supplements such as HEG and KEC improve knee osteoarthritis symptoms and can be a safe and effective treatment option for patients with osteoarthritis.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammed Bany Muhammad ◽  
Mohammed Yeasin
Keyword(s):  
Knee Osteoarthritis ◽  
Objective Assessment ◽  
Joint Space ◽  
Knee Joints ◽  
Ensemble Model ◽  
Scale Invariant ◽  
Substantial Impact ◽  
X Ray ◽  
Osteoarthritis Initiative

AbstractKnee 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.

White-Light Coronal Structures: Streamers and CMEs

1994 ◽  
Vol 144 ◽  
pp. 82
Author(s):  
E. Hildner
Keyword(s):  
Emission Line ◽  
Electron Density ◽  
White Light ◽  
Coronal Mass Ejections ◽  
X Rays ◽  
Solar Cycles ◽  
Temperature Function ◽  
X Ray ◽  
Eclipse Observations ◽  
Coronal Structures

AbstractOver the last twenty years, orbiting coronagraphs have vastly increased the amount of observational material for the whitelight corona. Spanning almost two solar cycles, and augmented by ground-based K-coronameter, emission-line, and eclipse observations, these data allow us to assess,inter alia: the typical and atypical behavior of the corona; how the corona evolves on time scales from minutes to a decade; and (in some respects) the relation between photospheric, coronal, and interplanetary features. This talk will review recent results on these three topics. A remark or two will attempt to relate the whitelight corona between 1.5 and 6 R⊙to the corona seen at lower altitudes in soft X-rays (e.g., with Yohkoh). The whitelight emission depends only on integrated electron density independent of temperature, whereas the soft X-ray emission depends upon the integral of electron density squared times a temperature function. The properties of coronal mass ejections (CMEs) will be reviewed briefly and their relationships to other solar and interplanetary phenomena will be noted.

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