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A Machine-Learning Approach to the Automated Assessment of Joint Synovitis Activity

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Computational Collective Intelligence (ICCCI 2016)

Abstract

Medical ultrasound imaging is an important tool in diagnosing and monitoring synovitis, which is an inflammation of the synovial membrane that surrounds a joint. Ultrasound images are examined by medical experts to assess the presence and progression of synovitis. Automating image analysis reduces the costs and increases the availability of the ultrasound diagnosis of synovitis and diminishes or eliminates subjective discrepancies. This article describes research that is concerned with the problem of the automatic estimation of the state of the activity of finger joint inflammation using the information that is present in ultrasonography imaging.

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Acknowledgement

This research obtained funding from the Norwegian Financial Mechanism 2009–2014 under Project Contract No. Pol-Nor/204256/16/2013. The ultrasound images for the MEDUSA project were created at the Section for Rheumatology; Department for Neurology, Rheumatology and Physical Medicine, 238 Central Hospital, Forde, Norway.

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Correspondence to Rafal Cupek .

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Wojciechowski, K. et al. (2016). A Machine-Learning Approach to the Automated Assessment of Joint Synovitis Activity. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_42

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  • DOI: https://doi.org/10.1007/978-3-319-45246-3_42

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