Juvenile idiopathic arthritis: a review of novel diagnostic and monitoring technologies

GARNER, Amelia J., SAATCHI, Reza, WARD, Oliver and HAWLEY, Daniel P. (2021). Juvenile idiopathic arthritis: a review of novel diagnostic and monitoring technologies. Healthcare, 9 (12).

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Official URL: https://www.mdpi.com/2227-9032/9/12/1683
Open Access URL: https://www.mdpi.com/2227-9032/9/12/1683/pdf (Published version)
Link to published version:: https://doi.org/10.3390/healthcare9121683
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    Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood and is characterized by an often insidious onset and a chronic relapsing−remitting course, once diagnosed. With successive flares of joint inflammation, joint damage accrues, often associated with pain and functional disability. The progressive nature and potential for chronic damage and disability caused by JIA emphasizes the critical need for a prompt and accurate diagnosis. This article provides a review of recent studies related to diagnosis, monitoring and management of JIA and outlines recent novel tools and techniques (infrared thermal imaging, three-dimensional imaging, accelerometry, artificial neural networks and fuzzy logic) which have demonstrated potential value in assessment and monitoring of JIA. The emergence of novel techniques to assist clinicians’ assessments for diagnosis and monitoring of JIA has demonstrated promise; however, further research is required to confirm their clinical utility.

    Item Type: Article
    Additional Information: ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 2227-9032 **History: published 04-12-2021; accepted 02-12-2021
    Uncontrolled Keywords: juvenile idiopathic arthritis, rheumatoid arthritis, artificial intelligence, thermal imaging, accelerometry, fuzzy logic
    Identification Number: https://doi.org/10.3390/healthcare9121683
    SWORD Depositor: Colin Knott
    Depositing User: Colin Knott
    Date Deposited: 06 Dec 2021 11:00
    Last Modified: 06 Dec 2021 11:00
    URI: http://shura.shu.ac.uk/id/eprint/29430

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