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dc.contributor.authorSchetinin, Vitaly
dc.date.accessioned2020-07-10T08:35:01Z
dc.date.available2020-07-10T00:00:00Z
dc.date.available2020-07-10T08:35:01Z
dc.date.issued2020-07-09
dc.identifier.citationSchetinin, V. (2020) 'Quantitative imaging for early detection of osteoarthritis'. Luton: University of Bedfordshire.en_US
dc.identifier.urihttp://hdl.handle.net/10547/624190
dc.description.abstractThe project supported by European Regional Development Fund is related to Quantitative Imaging for Early Detection of Osteoarthritis. The developed method has been tested on high resolution X-Ray images of knees at early stage when the pathological changes in patient's bones cannot be reliably quantified by using the standard radiologic tests. At early stage the pathology is latently developing and so being diagnosed later becomes untreatable. The proposed method has been developed in collaboration with Fusion Radiology (UK) and with Stavropol regional hospital (Russia). The Fusion Radiology (led by Mr Azizul Ambia) is a contractor of the NHS, providing radiology opinions for multiple UK hospitals. The regional hospital (the Deputy MD Anna Sadovaya) has verified the developed method on 160 patient cases. The new method has provided a statistically significant improvement of diagnostic accuracy on the anonymised patient records. The improvements were between 7% and 9%. The results achieved in the studies will allow radiologists to minimise false negative rate which is critically important for early diagnostics.en_US
dc.description.sponsorshipEuropean Regional Development Funden_US
dc.language.isoenen_US
dc.publisherUniversity of Bedfordshireen_US
dc.relation.ispartofseriesIB141en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectquantitative imaging for early detectionen_US
dc.subjectmachine learningen_US
dc.subjectSubject Categories::B821 Radiography, diagnosticen_US
dc.titleQuantitative imaging for early detection of osteoarthritisen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.date.updated2020-07-10T08:31:35Z
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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International