Automatic Quantification of Radiographic Wrist Joint Space Width of Patients With Rheumatoid Arthritis

Huo, Yinghe; Vincken, Koen L.; Heijde, Desirée van der; Hair, Maria J. H. de; Lafeber, Floris P. J. G.; Viergever, Max A.

doi:10.1109/tbme.2017.2659223

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…Enumeration of JSW using an automatic method for 3 wrist joints is presented by Yinghe Huo in [19] to detect Rheumatoid arthritis with 50 radiographs of patients which is applied with consecutive searches of separate path segments. But the method doesn't work with wrist affected by sclerosis diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Medical Diagnostic Systems for Arthritis

2019

IJRTE

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Arthritis is a chronic, an autoimmune disease which may affect in many critical health problems in patients. From the past few years, the number of patients suffering from arthritis are swiftly escalating. Until now there is no accurate treatment discovered for this rare disease. The paper discusses the different machine learning techniques in early detection of Arthritis so that, early diagnosis can help the patients to detect/ cure the disease.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Medical Diagnostic Systems for Arthritis

2019

IJRTE

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Identification of rheumatoid arthritis using bone image features with radial basis function neural networks

Bose,

Srinivasan,

Surenderanath

2024

Int J Imaging Syst Tech

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Rheumatoid arthritis (RA) leads to the destruction, deformation, and loss of function and causes joint damages to 85% of patients. The detection of bone mineral density from traditional x‐ray images consumes more time and it is observer dependent which decreases the evaluation performance when RA is in its early stage. Therefore, it is necessary to develop an observer‐independent computer‐aided automatic analysis system for evaluating JS narrowing. An efficient RA detection system based on feed‐forward neural network is proposed in this article. Initially the dataset is preprocessed to remove blur, redundant data and nonlinearities in RA images using wiener filter in less computation time. The edge boundaries are detected using nonlinear partial differential equation as the texture features of bones has great impact on the system accuracy. The detection strategy is implemented by optimized gray‐level co‐occurrence matrix based on bone image features. The selected features are optimized by genetic algorithm to improve the classification performance. The images are then classified as inflamed and noninflamed by radial basis function neural network. The effectiveness of the proposed classification approach is verified in terms of accuracy, sensitivity, and specificity and compared with the conventional convolutional neural network, artificial neural network, and support vector machine classifiers. Among the several existing techniques, our method found to be much effective with a maximum accuracy of 98.5% as some novel approaches are proposed and also tends to possess more compatible than the present system. Our computerized rheumatoid arteries detection approach will give more precise and flawless consistency rate.

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Design of an Ontology-Oriented Hand Rehabilitation Service Platform for Rheumatoid Arthritis Patients

2020

Advances in Intelligent Systems and Computing

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Automatic Quantification of Radiographic Wrist Joint Space Width of Patients With Rheumatoid Arthritis

Abstract: The proposed method may aid clinical researchers to study the progression of wrist joint damage in RA studies.

Cited by 8 publications

References 29 publications

Intelligent Medical Diagnostic Systems for Arthritis

Intelligent Medical Diagnostic Systems for Arthritis

Identification of rheumatoid arthritis using bone image features with radial basis function neural networks

Design of an Ontology-Oriented Hand Rehabilitation Service Platform for Rheumatoid Arthritis Patients

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