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Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment

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dc.contributor.author김은경-
dc.contributor.author이시은-
dc.date.accessioned2022-05-09T17:01:26Z-
dc.date.available2022-05-09T17:01:26Z-
dc.date.issued2022-04-
dc.identifier.issn0897-1889-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/188338-
dc.description.abstractWe evaluated and compared the mammographic density assessment of an artificial intelligence-based computer-assisted diagnosis (AI-CAD) program using inter-rater agreements between radiologists and an automated density assessment program. Between March and May 2020, 488 consecutive mammograms of 488 patients (56.2 ± 10.9 years) were collected from a single institution. We assigned four classes of mammographic density based on BI-RADS (Breast Imaging Reporting and Data System) using commercial AI-CAD (Lunit INSIGHT MMG), and compared inter-rater agreements between radiologists, AI-CAD, and another commercial automated density assessment program (Volpara®). The inter-rater agreement between AI-CAD and the reader consensus was 0.52 with a matched rate of 68.2% (333/488). The inter-rater agreement between Volpara® and the reader consensus was similar to AI-CAD at 0.50 with a matched rate of 62.7% (306/488). The inter-rater agreement between AI-CAD and Volpara® was 0.54 with a matched rate of 61.5% (300/488). In conclusion, density assessments by AI-CAD showed fair agreement with those of radiologists, similar to the agreement between the commercial automated density assessment program and radiologists.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSpringer-
dc.relation.isPartOfJOURNAL OF DIGITAL IMAGING-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHArtificial Intelligence-
dc.subject.MESHBreast Density*-
dc.subject.MESHBreast Neoplasms* / diagnostic imaging-
dc.subject.MESHComputers-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMammography / methods-
dc.subject.MESHRetrospective Studies-
dc.titleMammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorSi Eun Lee-
dc.contributor.googleauthorNak-Hoon Son-
dc.contributor.googleauthorMyung Hyun Kim-
dc.contributor.googleauthorEun-Kyung Kim-
dc.identifier.doi10.1007/s10278-021-00555-x-
dc.contributor.localIdA00801-
dc.contributor.localIdA05611-
dc.relation.journalcodeJ01379-
dc.identifier.eissn1618-727X-
dc.identifier.pmid35015180-
dc.identifier.urlhttps://link.springer.com/article/10.1007/s10278-021-00555-x-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordBreast density-
dc.subject.keywordDiagnosis, Computer-assisted-
dc.subject.keywordDigital mammography-
dc.contributor.alternativeNameKim, Eun Kyung-
dc.contributor.affiliatedAuthor김은경-
dc.contributor.affiliatedAuthor이시은-
dc.citation.volume35-
dc.citation.number2-
dc.citation.startPage173-
dc.citation.endPage179-
dc.identifier.bibliographicCitationJOURNAL OF DIGITAL IMAGING, Vol.35(2) : 173-179, 2022-04-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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