P107 Can a computer sense the symptoms of osteoarthritis from a radiograph? Initial findings from pilot AI approach to osteoarthritis assessment
P107 Can a computer sense the symptoms of osteoarthritis from a radiograph? Initial findings from pilot AI approach to osteoarthritis assessment
Osteoarthritis is the most common joint disease and is associated with substantial morbidity for the affected individual and a significant financial burden for the health system at large. There is a marked discrepancy between the extent of osteoarthritis observed via plain radiography and the magnitude of clinical symptoms. For this reason we aimed to investigate whether, using an artificial intelligence approach, we could train an algorithm to diagnose osteoarthritis and if we were able to find correlations between clinical symptoms and radiographic images.
Fuggle, Nicholas Rubek
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Pereira, Diogo
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Dennison, Elaine
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Cooper, Cyrus
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Mahmoodi, Sasan
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Fuggle, Nicholas Rubek
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Pereira, Diogo
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Dennison, Elaine
ee647287-edb4-4392-8361-e59fd505b1d1
Cooper, Cyrus
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Mahmoodi, Sasan
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Fuggle, Nicholas Rubek, Pereira, Diogo, Dennison, Elaine, Cooper, Cyrus and Mahmoodi, Sasan
(2021)
P107 Can a computer sense the symptoms of osteoarthritis from a radiograph? Initial findings from pilot AI approach to osteoarthritis assessment.
Rheumatology, 60 (1), [P107].
(doi:10.1093/rheumatology/keab247.104).
Abstract
Osteoarthritis is the most common joint disease and is associated with substantial morbidity for the affected individual and a significant financial burden for the health system at large. There is a marked discrepancy between the extent of osteoarthritis observed via plain radiography and the magnitude of clinical symptoms. For this reason we aimed to investigate whether, using an artificial intelligence approach, we could train an algorithm to diagnose osteoarthritis and if we were able to find correlations between clinical symptoms and radiographic images.
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e-pub ahead of print date: 26 April 2021
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Local EPrints ID: 475031
URI: http://eprints.soton.ac.uk/id/eprint/475031
ISSN: 1462-0324
PURE UUID: cab46171-9d8c-483c-827d-207df8262956
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Date deposited: 09 Mar 2023 17:33
Last modified: 18 Mar 2024 02:47
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Author:
Nicholas Rubek Fuggle
Author:
Diogo Pereira
Author:
Sasan Mahmoodi
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