2-Stage classification of knee joint thermograms for rheumatoid arthritis prediction in subclinical inflammation

Bardhan, Shawli; Bhowmik, Mrinal Kanti

doi:10.1007/s13246-019-00726-9

Cited by 22 publications

(6 citation statements)

References 30 publications

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“…Sensor data, which are rich datasets for disease diagnosis and monitoring, are acquired using technologies such as wearable devices, thermography sensors, and image sensors [89][90][91]. In a recent study, ML algorithms using features extracted from lymphocyte images generated by an electronic image sensor were highly accurate for RA classification, with accuracy rates as high as 97.5%.…”

Section: Using Omics Data In the Diagnosis Of Ramentioning

confidence: 99%

“…Furthermore, thermograms are noninvasive methods used to assess joint inflammation in RA [ 92 ]. Bardhan et al developed a two-stage classification algorithm correctly labeling nearly three-fourths of the knee thermograph scans (stage one was detection of arthritis-affected knees, and stage two was detection of knees affected by RA) [ 91 ].…”

Section: Artificial Intelligence In Ramentioning

confidence: 99%

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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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Section: Using Omics Data In the Diagnosis Of Ramentioning

confidence: 99%

Section: Artificial Intelligence In Ramentioning

confidence: 99%

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

2022

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“…Finally, the data source variability of medical imaging-based studies is high. Apart from well-extended image techniques such as Computed Tomography (CT), MRI, X-Rays (RX), Ultrasound (US); other less common image techniques such as optoacoustic [48], photomicrographs [49], smartphone photos [47], and thermograms [50] have also been employed in research studies.…”

Section: Classification Of Topics and Predictorsmentioning

confidence: 99%

“…The applicability of knee thermograms as a screening tool in the subclinical stages of RA diagnosis, and as a way to distinguish types of arthritis has been postulated by a group of Indian researchers [73]. Firstly, they employed different algorithms for inflamed region segmentation (e.g., k-means, Otsu).…”

Section: Imagementioning

confidence: 99%

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

Madrid

Merino‐Barbancho

Rodríguez-González

et al. 2022

Preprint

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The outstanding and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five-year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease classification, disease prediction, predictors identification, patient stratification and disease subtype identification, disease progression and activity, and treatment response. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed are highlighted in a discussion. Therefore, the present review arises with the aim of characterising how researchers are employing data science techniques in the rheumatology medical field.

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“…Likewise, studies of machine learning had attained successes in the prediction of RA, for instance, histologic data were used to establish machine learning model for stratifying synovial subtypes of RA (Orange et al, 2018); multiple features of RA and non-RA categorization including texture and shape were integrated for RA classification (Bardhan and Bhowmik, 2019).…”

Section: Machine Learning Model Validationmentioning

confidence: 99%

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2-Stage classification of knee joint thermograms for rheumatoid arthritis prediction in subclinical inflammation

Cited by 22 publications

References 30 publications

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

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

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

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