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Artificial Intelligence and Deep Learning in Musculoskeletal Magnetic Resonance Imaging

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dc.contributor.authorBaek, Seung Dae-
dc.contributor.authorLee, Joohee-
dc.contributor.authorKim, Sungjun-
dc.contributor.authorSong, Ho-Taek-
dc.contributor.authorLee, Young Han-
dc.date.accessioned2023-10-19T05:56:04Z-
dc.date.available2023-10-19T05:56:04Z-
dc.date.created2024-03-04-
dc.date.issued2023-06-
dc.identifier.issn2384-1095-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/196304-
dc.description.abstractThe application of artificial intelligence (AI) and deep learning (DL) in radiology is rapidly evolving. AI in healthcare has benefits for image recognition, classification, and radiological workflows from a clinical perspective. Additionally, clinical triage AI can be applied to triage systems. This review aims to introduce the concept of DL and discuss its applications in the interpretation of magnetic resonance (MR) images and the DL-based reconstruction of accelerated MR images, with an emphasis on musculoskeletal radiology. The most recent developments and future directions are also discussed briefly. © 2023 Korean Society of Magnetic Resonance in Medicine.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherKorean Society of Magnetic Resonance in Medicine-
dc.relation.isPartOfInvestigative Magnetic Resonance Imaging-
dc.relation.isPartOfInvestigative Magnetic Resonance Imaging-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleArtificial Intelligence and Deep Learning in Musculoskeletal Magnetic Resonance Imaging-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorBaek, Seung Dae-
dc.contributor.googleauthorLee, Joohee-
dc.contributor.googleauthorKim, Sungjun-
dc.contributor.googleauthorSong, Ho-Taek-
dc.contributor.googleauthorLee, Young Han-
dc.identifier.doi10.13104/imri.2022.1102-
dc.relation.journalcodeJ01186-
dc.identifier.eissn2384-1109-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordDeep learning-
dc.subject.keywordMagnetic resonance imaging-
dc.subject.keywordMusculoskeletal-
dc.contributor.alternativeNameKim, Sungjun-
dc.contributor.affiliatedAuthorBaek, Seung Dae-
dc.contributor.affiliatedAuthorLee, Joohee-
dc.contributor.affiliatedAuthorKim, Sungjun-
dc.contributor.affiliatedAuthorSong, Ho-Taek-
dc.contributor.affiliatedAuthorLee, Young Han-
dc.identifier.scopusid2-s2.0-85181974392-
dc.identifier.wosid001343297700001-
dc.citation.volume27-
dc.citation.number2-
dc.citation.startPage67-
dc.citation.endPage74-
dc.identifier.bibliographicCitationInvestigative Magnetic Resonance Imaging, Vol.27(2) : 67-74, 2023-06-
dc.identifier.rimsid82534-
dc.type.rimsART-
dc.description.journalClass1-
dc.description.journalClass1-
dc.subject.keywordAuthorArtificial intelligence-
dc.subject.keywordAuthorDeep learning-
dc.subject.keywordAuthorMagnetic resonance imaging-
dc.subject.keywordAuthorMusculoskeletal-
dc.subject.keywordPlusNEURAL FORAMINAL STENOSIS-
dc.subject.keywordPlusLUMBAR SPINE-
dc.subject.keywordPlusKNEE MRI-
dc.subject.keywordPlusDIAGNOSIS-
dc.subject.keywordPlusARTIFACTS-
dc.subject.keywordPlusIMAGES-
dc.subject.keywordPlusMODEL-
dc.type.docTypeReview-
dc.identifier.kciidART002968156-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalWebOfScienceCategoryRadiology, Nuclear Medicine & Medical Imaging-
dc.relation.journalResearchAreaRadiology, Nuclear Medicine & Medical Imaging-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
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