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Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

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dc.citedby7
dc.contributor.authorAlzubaidi L.en_US
dc.contributor.authorAL-Dulaimi K.en_US
dc.contributor.authorSalhi A.en_US
dc.contributor.authorAlammar Z.en_US
dc.contributor.authorFadhel M.A.en_US
dc.contributor.authorAlbahri A.S.en_US
dc.contributor.authorAlamoodi A.H.en_US
dc.contributor.authorAlbahri O.S.en_US
dc.contributor.authorHasan A.F.en_US
dc.contributor.authorBai J.en_US
dc.contributor.authorGilliland L.en_US
dc.contributor.authorPeng J.en_US
dc.contributor.authorBranni M.en_US
dc.contributor.authorShuker T.en_US
dc.contributor.authorCutbush K.en_US
dc.contributor.authorSantamar�a J.en_US
dc.contributor.authorMoreira C.en_US
dc.contributor.authorOuyang C.en_US
dc.contributor.authorDuan Y.en_US
dc.contributor.authorManoufali M.en_US
dc.contributor.authorJomaa M.en_US
dc.contributor.authorGupta A.en_US
dc.contributor.authorAbbosh A.en_US
dc.contributor.authorGu Y.en_US
dc.contributor.authorid57195380379en_US
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dc.contributor.authorid57196190467en_US
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dc.contributor.authorid57217198195en_US
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dc.contributor.authorid58925397700en_US
dc.contributor.authorid57216973352en_US
dc.contributor.authorid23992433600en_US
dc.contributor.authorid56211885400en_US
dc.contributor.authorid51663717600en_US
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dc.contributor.authorid57198676774en_US
dc.contributor.authorid13404674100en_US
dc.contributor.authorid7403046386en_US
dc.date.accessioned2025-03-03T07:42:18Z
dc.date.available2025-03-03T07:42:18Z
dc.date.issued2024
dc.description.abstractDeep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market. ? 2024 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102935
dc.identifier.doi10.1016/j.artmed.2024.102935
dc.identifier.scopus2-s2.0-85199786274
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85199786274&doi=10.1016%2fj.artmed.2024.102935&partnerID=40&md5=f27e678ec6e059d7834e8675e7e1984a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36412
dc.identifier.volume155
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Hybrid Gold Open Access
dc.sourceScopus
dc.sourcetitleArtificial Intelligence in Medicine
dc.subjectDeep Learning
dc.subjectHumans
dc.subjectOrthopedics
dc.subjectDeep learning
dc.subjectDiagnosis
dc.subjectFracture
dc.subjectBone tumor
dc.subjectDeep learning
dc.subjectFracture detection
dc.subjectKnowledge gaps
dc.subjectOrthopaedic applications
dc.subjectOrthopaedic surgeons
dc.subjectOrthopedic technology
dc.subjectOsteoarthritis
dc.subjectTrustworthy AI
dc.subjectTumor diagnosis
dc.subjectarthroplasty
dc.subjectbone age determination
dc.subjectbone implant
dc.subjectbone tumor
dc.subjectdeep learning
dc.subjectdiagnosis
dc.subjectfairness
dc.subjectFood and Drug Administration
dc.subjectfracture
dc.subjecthuman
dc.subjectknowledge gap
dc.subjectMRI scanner
dc.subjectnuclear magnetic resonance imaging
dc.subjectorthopedic surgeon
dc.subjectorthopedics
dc.subjectosteoarthritis
dc.subjectprediction
dc.subjectreview
dc.subjectsoft tissue disease
dc.subjecttherapy
dc.subjecttreatment planning
dc.subjecttrustworthiness
dc.subjecttumor diagnosis
dc.subjectWeb of Science
dc.subjectprocedures
dc.subjectOrthopedics
dc.titleComprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusionen_US
dc.typeReviewen_US
dspace.entity.typePublication
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