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Application of artificial intelligence in capsule endoscopy: Where are we now?

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dc.contributor.authorHwang Y.-
dc.contributor.authorPark J.-
dc.contributor.authorLim Y.J.-
dc.contributor.authorChun H.J.-
dc.date.available2020-11-02T07:57:42Z-
dc.date.issued2018-
dc.identifier.issn2234-2400-
dc.identifier.issn2234-2443-
dc.identifier.urihttps://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/4203-
dc.description.abstractUnlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning–based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning–based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy. © 2018 Korean Society of Gastrointestinal Endoscopy.-
dc.format.extent5-
dc.language영어-
dc.language.isoENG-
dc.publisherKorean Society of Gastrointestinal Endoscopy-
dc.titleApplication of artificial intelligence in capsule endoscopy: Where are we now?-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.5946/ce.2018.173-
dc.identifier.scopusid2-s2.0-85057719067-
dc.identifier.bibliographicCitationClinical Endoscopy, v.51, no.6, pp 547 - 551-
dc.citation.titleClinical Endoscopy-
dc.citation.volume51-
dc.citation.number6-
dc.citation.startPage547-
dc.citation.endPage551-
dc.type.docTypeReview-
dc.identifier.kciidART002409419-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClassesci-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorArtificial intelligence-
dc.subject.keywordAuthorCapsule endoscopy-
dc.subject.keywordAuthorDeep learning-
dc.subject.keywordAuthorLesion detection-
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