Diagnosis and classification of axial spondyloarthritis

2016 ◽  
Author(s):  
Floris van Gaalen ◽  
Désirée van der Heijde ◽  
Maxime Dougados
Keyword(s):  
Chronic Back Pain ◽  
Axial Spondyloarthritis ◽  
Chronic Inflammatory Disease ◽  
Disease Classification ◽  
Laboratory Features ◽  
Bone Changes ◽  
Diagnosis And Classification ◽  
The Difference ◽  
Magnetic Resonance Imaging Mri

Axial spondyloarthritis (axSpA) is a potentially disabling chronic inflammatory disease affecting the spine and sacroiliac (SI) joints. Lead symptoms are chronic back pain and stiffness. The disease is called radiographic axSpA or ankylosing spondylitis (AS) when, on plain radiographs, bone changes consistent with sacroiliitis are present. When no evidence of sacroiliitis is seen on radiographs, it is called non-radiographic axSpA. In such cases, diagnosis is made based on evidence of active inflammation of SI joints on magnetic resonance imaging (MRI) and clinical and laboratory features, or a combination of clinical and laboratory features only. Apart from affecting the spine and SI joints, axSpA may involve peripheral joints (e.g. knee, ankle) and manifest in extra-articular manifestations, for example uveitis, psoriasis, and inflammatory bowel disease. In this chapter, diagnosis and classification of axSpA is discussed, including use of MRI in detecting sacroiliitis and the difference between clinical diagnosis and disease classification.

Diagnosis and classification criteria

2018 ◽  
Author(s):  
Laura C. Coates ◽  
William J. Taylor
Keyword(s):  
Signs And Symptoms ◽  
Classification Criteria ◽  
Design Function ◽  
Diagnosis And Classification ◽  
Axial Disease ◽  
Different Populations ◽  
The Difference ◽  
And Performance ◽  
Over Time

This chapter covers diagnosis and classification of psoriatic arthritis (PsA). Firstly the difference between diagnosis and classification criteria in terms of their design, function, and performance is discussed. The diagnostic clues of PsA are summarized: risk factors for development of arthritis amongst patients with psoriasis, signs, and symptoms of articular, entheseal and axial disease, and relevant investigations. Older classification criteria for PsA are discussed along with later modifications. The development of the CASPAR criteria is described and subsequent studies assessing the accuracy of the CASPAR criteria in different populations are then summarized. How PsA fits within the broader family of spondyloarthritides (SpA) and the performance of CASPAR compared to SpA criteria is outlined. Different subtypes of PsA, as well as the evolution of individual patients through subtypes over time, are described. Finally future proposals to develop the ‘stem’ of CASPAR to define ‘inflammatory articular, entheseal or axial disease’ are summarized.

Advancing Early Identification of Axial Spondyloarthritis: An Interobserver Comparison of Extended Role Practitioners and Rheumatologists

2019 ◽  
Vol 47 (4) ◽  
pp. 524-530 ◽  
Author(s):  
Laura Passalent ◽  
Christopher Hawke ◽  
Daeria O. Lawson ◽  
Ahmed Omar ◽  
Khalid A. Alnaqbi ◽  
...  
Keyword(s):  
Back Pain ◽  
Chronic Back Pain ◽  
Axial Spondyloarthritis ◽  
Age Of Onset ◽  
Clinical Information ◽  
Models Of Care ◽  
Clinical Impression ◽  
Written Form ◽  
Mechanical Back Pain ◽  
Magnetic Resonance Imaging Mri

Objective.To compare clinical impression and confidence of extended role practitioners (ERP) with those of rheumatologists experienced in axial spondyloarthritis (axSpA) according to (1) evaluation of patients with chronic back pain assessed for axSpA; and (2) magnetic resonance imaging (MRI) recommendation for further investigation of these patients.Methods.Patients with ≥ 3 months of back pain and age of onset < 45 years were referred for axSpA evaluation. An ERP assessed consecutive patients and recorded standardized clinical information in written form. Three rheumatologists subsequently evaluated each patient based on the recorded information. Patients were classified as having axSpA or mechanical back pain based on clinical and investigative findings. Level of confidence was noted for classification and MRI indication. Agreement between assessors was evaluated using percentage agreement and κ coefficient.Results.Fifty-seven patients were assessed. Interobserver agreement of clinical impression for all raters was moderate (κ = 0.52). Agreement of clinical impression between ERP and rheumatologists ranged between 71.2% (κ = 0.41) and 79.7% (κ = 0.57). Agreement of clinical impression among rheumatologists ranged from 74.1% (κ = 0.49) to 79.7% (κ = 0.58). All rater agreement for MRI indication was fair (κ = 0.37). ERP agreement with rheumatologist for MRI recommendation ranged from 64.2% (κ = 0.32) to 75% (κ = 0.48). Agreement for MRI indication among rheumatologists ranged from 62.9% (κ = 0.27) to 74% (κ = 0.47). Confidence in clinical impression was similar among all practitioners.Conclusion.ERP with specialty training in inflammatory arthritis demonstrate clinical impressions comparable with those of rheumatologists in the assessment of axSpA. Incorporation of such roles into existing models of care may assist in early detection of axSpA.

Automated Brain Imaging Diagnosis and Classification Model using Rat Swarm Optimization with Deep Learning based Capsule Network

2021 ◽  
pp. 2240001
Author(s):  
A. Vasantharaj ◽  
Pacha Shoba Rani ◽  
Sirajul Huque ◽  
K. S. Raghuram ◽  
R. Ganeshkumar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Model ◽  
Swarm Optimization ◽  
Diagnosis And Classification ◽  
Enhance Efficiency ◽  
Magnetic Resonance Imaging Mri ◽  
Learning Machine ◽  
The Brain

Earlier identification of brain tumor (BT) is essential to increase the survival rate of the patients. The commonly used imaging technique for BT diagnosis is magnetic resonance imaging (MRI). Automated BT classification model is required for assisting the radiologists to save time and enhance efficiency. The classification of BT is difficult owing to the non-uniform shapes of tumors and location of tumors in the brain. Therefore, deep learning (DL) models can be employed for the effective identification, prediction, and diagnosis of diseases. In this view, this paper presents an automated BT diagnosis using rat swarm optimization (RSO) with deep learning based capsule network (DLCN) model, named RSO-DLCN model. The presented RSO-DLCN model involves bilateral filtering (BF) based preprocessing to enhance the quality of the MRI. Besides, non-iterative grabcut based segmentation (NIGCS) technique is applied to detect the affected tumor regions. In addition, DLCN model based feature extractor with RSO algorithm based parameter optimization processes takes place. Finally, extreme learning machine with stacked autoencoder (ELM-SA) based classifier is employed for the effective classification of BT. For validating the BT diagnostic performance of the presented RSO-DLCN model, an extensive set of simulations were carried out and the results are inspected under diverse dimensions. The simulation outcome demonstrated the promising results of the RSO-DLCN model on BT diagnosis with the sensitivity of 98.4%, specificity of 99%, and accuracy of 98.7%.

scholarly journals Molecular Basis of Human Enamel Defects

2014 ◽  
Vol 18 (1) ◽  
pp. 5-16
Author(s):  
Georgios Chatzopoulos ◽  
Dimitrios Tziafas
Keyword(s):  
Molecular Basis ◽  
Structural Changes ◽  
Clinical Presentation ◽  
Disease Classification ◽  
Amelogenesis Imperfecta ◽  
Gene Products ◽  
Enamel Defects ◽  
Human Enamel ◽  
Diagnosis And Classification

Abstract During eruption of teeth in the oral cavity, the effect of gene variations and environmental factors can result in morphological and structural changes in teeth. Amelogenesis imperfecta is a failure which is detected on the enamel of the teeth and clinical picture varies by the severity and type of the disease. Classification of the types of amelogenesis imperfecta is determined by histological, genetic, clinical and radiographic criteria. Specifically, there are 4 types of amelogenesis imperfecta (according to Witkop): hypoplastic form, hypo-maturation form, hypo-calcified form, and hypo-maturation/hypoplasia form with taurodontism and 14 subcategories. The diagnosis and classification of amelogenesis imperfecta has traditionally been based on clinical presentation or phenotype and the inheritance pattern. Several genes can be mutated and cause the disease. Millions of genes, possibly more than 10,000 genes produce proteins that regulate synthesis of enamel. Some of the genes and gene products that are likely associated with amelogenesis imperfecta are: amelogenin (AMELX, AMELY genes), ameloblastin (AMBN gene), enamelin (ENAM gene), enamelysin (MMP20 gene), kalikryn 4 (KLK 4 gene), tuftelins (Tuftelin gene), FAM83H (FAM83H gene) and WDR72 (WDR72 gene). Particular attention should be given by the dentist in recognition and correlation of phenotypes with genotypes, in order to diagnose quickly and accurately such a possible disease and to prevent or treat it easily and quickly. Modern dentistry should restore these lesions in order to guarantee aesthetics and functionality, usually in collaboration with a group of dentists.

S272 – Diagnostic and Staging Accuracy of MRI vs. CT in CRS

2008 ◽  
Vol 139 (2_suppl) ◽  
pp. P166-P166
Author(s):  
Harrison W Lin ◽  
Neil Bhattacharyya
Keyword(s):  
Disease Classification ◽  
Time Interval ◽  
Sinus Disease ◽  
Time Period ◽  
Staging Accuracy ◽  
Kappa Value ◽  
Mri Scans ◽  
The Difference ◽  
Magnetic Resonance Imaging Mri ◽  
Ct And Mri

Objectives Determine the correlation between computed tomography (CT)- and magnetic resonance imaging (MRI)-based staging and classification of chronic rhinosinusitis (CRS). Methods Paired CT and MRI scans of 89 adult patients who were imaged by both modalities within a 3-month time period for evaluation of pituitary disease were scored for sinus disease using the Lund-Mackay system in a randomized and blinded fashion. The Lund scores were compared for similarity, correlation, and diagnostic classification between modalities. Results The mean Lund scores were 2.3 ± 0.6 (95% CI) for CT-based staging and 2.1 ± 0.5 for MRI-based staging with a median time interval between scans of 3 days. The difference in means was not statistically significant (p=0.444, paired t-test). Correlation analysis revealed a significant association between CT- and MRI-based scores (Pearson's r=0.837, p<0.001). Disease classification agreement analysis using published Lund score cutoffs (3 versus 4) for the likelihood of true sinus disease revealed that CT- and MRI-based scoring agreed on 76 cases (85.4%). Disagreement in disease classification occurred in 13 cases (7 MRI positive but CT negative and 6 CT positive but MRI negative) for a kappa value of 0.557 (p<0.001). Conclusions Lund-Mackay staging of sinus disease by MRI is closely correlated to corresponding staging based on CT. MRI does not significantly over-stage or over-classify patients with sinus disease.

Defining active sacroiliitis on MRI for classification of axial spondyloarthritis: update by the ASAS MRI working group

2016 ◽  
Vol 75 (11) ◽  
pp. 1958-1963 ◽  
Author(s):  
Robert G W Lambert ◽  
Pauline A C Bakker ◽  
Désirée van der Heijde ◽  
Ulrich Weber ◽  
Martin Rudwaleit ◽  
...  
Keyword(s):  
Working Group ◽  
Structural Damage ◽  
Axial Spondyloarthritis ◽  
Imaging Features ◽  
Mri Features ◽  
Asas Classification Criteria ◽  
Diagnosis And Classification ◽  
Active Sacroiliitis ◽  
Definition Of

ObjectivesTo review and update the existing definition of a positive MRI for classification of axial spondyloarthritis (SpA).MethodsThe Assessment in SpondyloArthritis International Society (ASAS) MRI working group conducted a consensus exercise to review the definition of a positive MRI for inclusion in the ASAS classification criteria of axial SpA. Existing definitions and new data relevant to the MRI diagnosis and classification of sacroiliitis and spondylitis in axial SpA, published since the ASAS definition first appeared in print in 2009, were reviewed and discussed. The precise wording of the existing definition was examined in detail and the data and a draft proposal were presented to and voted on by the ASAS membership.ResultsThe clear presence of bone marrow oedema on MRI in subchondral bone is still considered to be the defining observation that determines the presence of active sacroiliitis. Structural damage lesions seen on MRI may contribute to a decision by the observer that inflammatory lesions are genuinely due to SpA but are not required to meet the definition. The existing definition was clarified adding guidelines and images to assist in the application of the definition.ConclusionThe definition of a positive MRI for classification of axial SpA should continue to primarily depend on the imaging features of ‘active sacroiliitis’ until more data are available regarding MRI features of structural damage in the sacroiliac joint and MRI features in the spine and their utility when used for classification purposes.

scholarly journals Interobserver Agreement in Magnetic Resonance of the Sacroiliac Joints in Patients with Spondyloarthritis

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Juan C. Rueda ◽  
Sofia Arias-Correal ◽  
Andres Y. Vasquez ◽  
Enrique Calvo ◽  
Paola Peña ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Interobserver Agreement ◽  
Clinical Laboratory ◽  
Musculoskeletal Diseases ◽  
Sacroiliac Joints ◽  
Mri Findings ◽  
Expert Radiologist ◽  
Diagnosis And Classification ◽  
Magnetic Resonance Imaging Mri

Background. Clinical, laboratory, and radiologic parameters are used for diagnosis and classification of spondyloarthritis (SpA). Magnetic resonance imaging (MRI) of sacroiliac (SI) joints is being increasingly used to detect early sacroiliitis. We decided to evaluate the interobserver agreement in MRI findings of SI joints of SpA patients between a local radiologist, a rheumatologist, and an expert radiologist in musculoskeletal diseases. Methods. 66 MRI images of the SI joints of patients with established diagnosis of SpA were evaluated. Agreement was expressed in Cohen’s kappa. Results. Interobserver agreement between a local radiologist and an expert radiologist was fair (κ=0.37). Only acute findings showed a moderate agreement (κ=0.45), while chronic findings revealed 76.5% of disagreement (κ=0.31). A fair agreement was observed in acute findings (κ=0.38) as well as chronic findings (κ=0.38) between a local radiologist and a rheumatologist. There was a substantial agreement between an expert radiologist and a rheumatologist (κ=0.73). In acute findings, a 100% agreement was achieved. Also chronic and acute plus chronic findings showed high levels of agreement (κ=0.73 and 0.62, resp.). Conclusions. Our study shows that rheumatologists may have similar MRI interpretations of SI joints in SpA patients as an expert radiologist.

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