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손목터널증후군 초음파 진단의 최신 지견

DC Field Value Language
dc.contributor.author이상철-
dc.date.accessioned2026-03-10T01:57:10Z-
dc.date.available2026-03-10T01:57:10Z-
dc.date.created2026-03-10-
dc.date.issued2025-12-
dc.identifier.issn1598-5490-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/211029-
dc.description.abstractCarpal tunnel syndrome (CTS) is the most common entrapment neuropathy of the upper extremity. While nerve conduction studies (NCS) have traditionally been the gold standard for diagnosis, high-resolution ultrasound has emerged as a powerful, non-invasive, and cost-effective alternative. This review provides a comprehensive overview of the latest advances in the ultrasound diagnosis of CTS. The review begins with an evaluation of B-mode ultrasound, focusing on the cross-sectional area (CSA) of the median nerve as the most robust diagnostic parameter, while also discussing its limitations, such as the variability of cutoff values. We then explore the incremental diagnostic value of advanced functional techniques. Doppler ultrasound provides insights into nerve vascularity, and elastography offers a quantitative measure of nerve stiffness, which has shown superiority over CSA in grading disease severity. Dynamic ultrasound and speckle tracking assess nerve mobility, directly visualizing the pathomechanical aspects of entrapment. Finally, we discuss the transformative potential of artificial intelligence (AI) and radiomics, which can extract sub-perceptual features from ultrasound images to enhance diagnostic accuracy, objectivity, and reproducibility, particularly in mild or equivocal cases.-
dc.formatapplication/pdf-
dc.languagekorean-
dc.publisher대한임상통증학회-
dc.relation.isPartOfClinical Pain(대한임상통증학회지)-
dc.title손목터널증후군 초음파 진단의 최신 지견-
dc.title.alternativeRecent Advances in the Ultrasound Diagnosis of Carpal Tunnel Syndrome-
dc.typeArticle-
dc.contributor.googleauthor이상철-
dc.identifier.doi10.35827/cp.2025.24.2.100-
dc.relation.journalcodeJ00604-
dc.subject.keywordUltrasound-
dc.subject.keywordCarpal tunnel syndrome-
dc.subject.keywordMedina nerve-
dc.subject.keywordCross-sectional area-
dc.subject.keywordArtificial intelligence-
dc.contributor.affiliatedAuthor이상철-
dc.citation.volume24-
dc.citation.number2-
dc.citation.startPage100-
dc.citation.endPage105-
dc.identifier.bibliographicCitationClinical Pain(대한임상통증학회지), Vol.24(2) : 100-105, 2025-12-
dc.identifier.rimsid91883-
dc.type.rimsART-
dc.description.journalClass2-
dc.description.journalClass2-
dc.subject.keywordAuthorUltrasound-
dc.subject.keywordAuthorCarpal tunnel syndrome-
dc.subject.keywordAuthorMedina nerve-
dc.subject.keywordAuthorCross-sectional area-
dc.subject.keywordAuthorArtificial intelligence-
dc.type.docTypeY-
dc.identifier.kciidART003282204-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClasskci-
dc.description.journalRegisteredClassother-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Rehabilitation Medicine (재활의학교실) > 1. Journal Papers

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