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Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging

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dc.contributor.author유선국-
dc.date.accessioned2021-12-28T17:14:47Z-
dc.date.available2021-12-28T17:14:47Z-
dc.date.issued2021-03-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/187004-
dc.description.abstractMedical imaging is currently being applied in artificial intelligence and big data technologies in data formats. In order for medical imaging collected from different institutions and systems to be used for artificial intelligence data, interoperability is becoming a key element. Whilst interoperability is currently guaranteed through medical data standards, compliance to personal information protection laws, and other methods, a standard solution for measurement values is deemed to be necessary in order for further applications as artificial intelligence data. As a result, this study proposes a model for interoperability in medical data standards, personal information protection methods, and medical imaging measurements. This model applies Health Level Seven (HL7) and Digital Imaging and Communications in Medicine (DICOM) standards to medical imaging data standards and enables increased accessibility towards medical imaging data in the compliance of personal information protection laws through the use of de-identifying methods. This study focuses on offering a standard for the measurement values of standard materials that addresses uncertainty in measurements that pre-existing medical imaging measurement standards did not provide. The study finds that medical imaging data standards conform to pre-existing standards and also provide protection to personal information within any medical images through de-identifying methods. Moreover, it proposes a reference model that increases interoperability by composing a process that minimizes uncertainty using standard materials. The interoperability reference model is expected to assist artificial intelligence systems using medical imaging and further enhance the resilience of future health technologies and system development.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherMDPI AG-
dc.relation.isPartOfAPPLIED SCIENCES-BASEL-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleInteroperability Reference Models for Applications of Artificial Intelligence in Medical Imaging-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Medical Engineering (의학공학교실)-
dc.contributor.googleauthorOyun Kwon-
dc.contributor.googleauthorSun K. Yoo-
dc.identifier.doi10.3390/app11062704-
dc.contributor.localIdA02471-
dc.relation.journalcodeJ03706-
dc.identifier.eissn2076-3417-
dc.subject.keywordinteroperability-
dc.subject.keywordde-identifiers-
dc.subject.keywordmeasurement uncertainty-
dc.subject.keywordstandardization-
dc.contributor.alternativeNameYoo, Sun Kook-
dc.contributor.affiliatedAuthor유선국-
dc.citation.volume11-
dc.citation.number6-
dc.citation.startPage2704-
dc.identifier.bibliographicCitationAPPLIED SCIENCES-BASEL, Vol.11(6) : 2704, 2021-03-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers

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