Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/103872
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dc.contributorDepartment of Biomedical Engineeringen_US
dc.creatorYick, HTVen_US
dc.creatorChan, PKen_US
dc.creatorWen, Cen_US
dc.creatorFung, WCen_US
dc.creatorYan, CHen_US
dc.creatorChiu, KYen_US
dc.date.accessioned2024-01-10T02:41:07Z-
dc.date.available2024-01-10T02:41:07Z-
dc.identifier.issn2210-4917en_US
dc.identifier.urihttp://hdl.handle.net/10397/103872-
dc.language.isoenen_US
dc.publisherSage Publications Ltd.en_US
dc.rights© The Author(s) 2022en_US
dc.rightsCreative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).en_US
dc.rightsThe following publication Yick, H. T. V., Chan, P. K., Wen, C., Fung, W. C., Yan, C. H., & Chiu, K. Y. (2022). Artificial intelligence reshapes current understanding and management of osteoarthritis: A narrative review. Journal of Orthopaedics, Trauma and Rehabilitation, 29(1), 1-8 is available at https://doi.org/10.1177/22104917221082315.en_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectOsteoarthritisen_US
dc.subjectDeep learningen_US
dc.titleArtificial intelligence reshapes current understanding and management of osteoarthritis : a narrative reviewen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage8en_US
dc.identifier.volume29en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1177/22104917221082315en_US
dcterms.abstractCurrent practice of osteoarthritis has its insufficiencies which researchers are tackling with artificial intelligence (AI). This article discusses three kinds of AI models, namely diagnostic models, prediction models and morphological models. Diagnostic models enhance efficiency in diagnosis by providing an automated algorithm in knee images processing. Prediction models utilize behavioral and radiological data to assess the risk of osteoarthritis before symptom onset and needs to perform surgery. Morphological models detect biomechanical changes to facilitate understanding of pathophysiology and provide personalized intervention. Through reviewing present evidence, we demonstrate that AI could assist doctors in diagnosis, predict osteoarthritis and guide future research.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of orthopaedics, trauma and rehabilitation, June 2022, v. 29, no. 1, p. 1-8en_US
dcterms.isPartOfJournal of orthopaedics, trauma and rehabilitationen_US
dcterms.issued2022-06-
dc.identifier.isiWOS:000928123000005-
dc.identifier.scopus2-s2.0-85128405269-
dc.identifier.eissn2210-4925en_US
dc.description.validate202401 bcvcen_US
dc.description.oaVersion of Recorden_US
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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