Automated assessment of joint synovitis activity from medical ultrasound and power doppler examinations using image processing and machine learning methods

Cupek, Rafał; Ziębiński, Adam

doi:10.5114/reum.2016.63664

Cited by 14 publications

(12 citation statements)

References 8 publications

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“…Notwithstanding the wide utilization of radiography in joint assessment, PC supported indicative ultrasound frameworks have been created to arrange intra-and extra-articular pathologies with the likeness of joint space width appraisal [32] and joint aggravation assessment [33]. Additionally, the semiquantitative ultrasound evaluation of synovitis seriousness assists with deciding the infection action of RA presented in [20,34]. RA assessment is presented as an issue of image arrangement [35,36].…”

Section: Image Analysismentioning

confidence: 99%

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Mate

Kureshi²,

Singh

2021

Journal of Healthcare Engineering

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Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.

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Section: Image Analysismentioning

confidence: 99%

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Mate

Kureshi²,

Singh

2021

Journal of Healthcare Engineering

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“…Late imaging findings may include subluxation or luxation, scar formation, fibrosis, and bony ankylosis [66]. To the best of our knowledge, AI-based models have been exploited in the detection of synovitis [69][70][71], bone erosions [72,73], bone marrow edema [74], and joint space narrowing [75]. However, we did not find investigations on other features, such as subcortical cysts, joint effusion, or late imaging findings.…”

Section: Using Omics Data In the Diagnosis Of Ramentioning

confidence: 77%

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

2022

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Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of Sara Momtazmanesh and Ali Nowroozi have contributed equally to this work and share first authorship.

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“…Thus, automatic analysis of the images may be helpful to monitor the disease in RA patients. The ML methods used to this end are mostly artificial neural networks (58,59), and especially deep learning (60,61).…”

Section: Monitoring the Diseasementioning

confidence: 99%

Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions

Kedra

Davergne

Braithwaite³

et al. 2021

Expert Review of Clinical Immunology

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Introduction: Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or "omics" data). Such approaches may improve the management of patients with RA. Areas covered: In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: "arthritis, rheumatoid" and "machine learning". Based on this search, the usefulness of ML methods for RA diagnosis, monitoring and prediction of response to treatment and RA outcomes, is discussed. Expert opinion: ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers and physicians is therefore required to move this field forward.

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Automated assessment of joint synovitis activity from medical ultrasound and power doppler examinations using image processing and machine learning methods

Cited by 14 publications

References 8 publications

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions

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