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Artificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography

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dc.contributor.author윤정현-
dc.contributor.author이시은-
dc.contributor.author한경화-
dc.date.accessioned2023-07-12T02:45:25Z-
dc.date.available2023-07-12T02:45:25Z-
dc.date.issued2023-05-
dc.identifier.issn0284-1851-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/195388-
dc.description.abstractBackground: Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views. Purpose: To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view. Material and Methods: From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included. Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance (P < 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P =0.002; dense breasts: 33.6% to 21.2%, P < 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P =0.234). The reader-averaged specificity (71.2% to 79.8%, P < 0.001) and accuracy (71.3% to 79.7%, P<0.001) significantly improved with AI assistance. Conclusion: AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSage-
dc.relation.isPartOfACTA RADIOLOGICA-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHArtificial Intelligence*-
dc.subject.MESHBreast / diagnostic imaging-
dc.subject.MESHBreast Density-
dc.subject.MESHBreast Neoplasms* / diagnostic imaging-
dc.subject.MESHEarly Detection of Cancer-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMammography-
dc.subject.MESHRetrospective Studies-
dc.titleArtificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorSi Eun Lee-
dc.contributor.googleauthorGa Ram Kim-
dc.contributor.googleauthorJung Hyun Yoon-
dc.contributor.googleauthorKyunghwa Han-
dc.contributor.googleauthorWon Jeong Son-
dc.contributor.googleauthorHye Jung Shin-
dc.contributor.googleauthorHee Jung Moon-
dc.identifier.doi10.1177/02841851221140556-
dc.contributor.localIdA02595-
dc.contributor.localIdA05611-
dc.contributor.localIdA04267-
dc.relation.journalcodeJ00033-
dc.identifier.eissn1600-0455-
dc.identifier.pmid36426409-
dc.identifier.urlhttps://journals.sagepub.com/doi/10.1177/02841851221140556-
dc.subject.keywordDigital mammography-
dc.subject.keywordartificial intelligence-
dc.subject.keywordbreast neoplasms-
dc.subject.keywordcomputer-assisted-
dc.subject.keyworddiagnosis-
dc.contributor.alternativeNameYoon, Jung Hyun-
dc.contributor.affiliatedAuthor윤정현-
dc.contributor.affiliatedAuthor이시은-
dc.contributor.affiliatedAuthor한경화-
dc.citation.volume64-
dc.citation.number5-
dc.citation.startPage1808-
dc.citation.endPage1815-
dc.identifier.bibliographicCitationACTA RADIOLOGICA, Vol.64(5) : 1808-1815, 2023-05-
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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