Can a computer sense the symptoms of osteoarthritis from a radiograph? Initial findings from a pilot, AI approach to osteoarthritis assessment
Can a computer sense the symptoms of osteoarthritis from a radiograph? Initial findings from a 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.
British Society for Rheumatology
Pereira, Diogo
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Fuggle, Nick
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Mahmoodi, Sasan
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Cooper, Cyrus
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Pereira, Diogo
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Fuggle, Nick
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Mahmoodi, Sasan
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Cooper, Cyrus
e05f5612-b493-4273-9b71-9e0ce32bdad6
Pereira, Diogo, Fuggle, Nick, Mahmoodi, Sasan and Cooper, Cyrus
(2021)
Can a computer sense the symptoms of osteoarthritis from a radiograph? Initial findings from a pilot, AI approach to osteoarthritis assessment.
In Abstract in Rheumatology.
vol. 16,
British Society for Rheumatology.
2 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
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.
Text
BSR21 OA Computer Vision abstract 20_10_07
More information
Accepted/In Press date: 8 February 2021
Venue - Dates:
British Society of Rheumatology Annual Conference, , Virtual, 2021-04-26 - 2021-04-28
Identifiers
Local EPrints ID: 446462
URI: http://eprints.soton.ac.uk/id/eprint/446462
PURE UUID: 4a7ea599-c496-4ca7-98ef-f4276ba7c1e6
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Date deposited: 10 Feb 2021 17:33
Last modified: 17 Mar 2024 06:19
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Contributors
Author:
Diogo Pereira
Author:
Sasan Mahmoodi
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