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Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children

DC FieldValueLanguage
dc.contributor.author김명준-
dc.contributor.author김성원-
dc.contributor.author김형철-
dc.contributor.author신재승-
dc.contributor.author신현주-
dc.contributor.author윤자경-
dc.contributor.author윤혜성-
dc.contributor.author이미정-
dc.contributor.author한경화-
dc.date.accessioned2020-02-11T06:34:11Z-
dc.date.available2020-02-11T06:34:11Z-
dc.date.issued2019-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/174728-
dc.description.abstractThe purpose of this study was to develop and test the performance of a deep learning-based algorithm to detect ileocolic intussusception using abdominal radiographs of young children. For the training set, children (≤5 years old) who underwent abdominal radiograph and ultrasonography (US) for suspicion of intussusception from March 2005 to December 2017 were retrospectively included and divided into control and intussusception groups according to the US results. A YOLOv3-based algorithm was developed to recognize the rectangular area of the right abdomen and to diagnose intussusception. For the validation set, children (≤5 years old) who underwent both radiograph and US from January to August 2018 with the suspicion of intussusception were included. Diagnostic performances of an algorithm and radiologists were compared. Total 681 children including 242 children in intussusception group were included in the training set and 75 children including 25 children in intussusception group were included in the validation set. The sensitivity of the algorithm was higher compared with that of the radiologists (0.76 vs. 0.46, p = 0.013), while specificity was not different between the algorithm and the radiologists (0.96 vs. 0.92, p = 0.32). Deep learning-based algorithm can aid screening of intussusception using abdominal radiography in young children.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titlePerformance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorSungwon Kim-
dc.contributor.googleauthorHaesung Yoon-
dc.contributor.googleauthorMi-Jung Lee-
dc.contributor.googleauthorMyung-Joon Kim-
dc.contributor.googleauthorKyunghwa Han-
dc.contributor.googleauthorJa Kyung Yoon-
dc.contributor.googleauthorHyung Cheol Kim-
dc.contributor.googleauthorJaeseung Shin-
dc.contributor.googleauthorHyun Joo Shin-
dc.identifier.doi10.1038/s41598-019-55536-6-
dc.contributor.localIdA00425-
dc.contributor.localIdA05309-
dc.contributor.localIdA05309-
dc.contributor.localIdA05771-
dc.contributor.localIdA05771-
dc.contributor.localIdA05599-
dc.contributor.localIdA05599-
dc.contributor.localIdA02178-
dc.contributor.localIdA02178-
dc.contributor.localIdA05487-
dc.contributor.localIdA05487-
dc.contributor.localIdA04989-
dc.contributor.localIdA04989-
dc.contributor.localIdA02774-
dc.contributor.localIdA02774-
dc.contributor.localIdA04267-
dc.contributor.localIdA04267-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid31857641-
dc.contributor.alternativeNameKim, Myung Joon-
dc.contributor.affiliatedAuthor김명준-
dc.contributor.affiliatedAuthor김성원-
dc.contributor.affiliatedAuthor김성원-
dc.contributor.affiliatedAuthor김형철-
dc.contributor.affiliatedAuthor김형철-
dc.contributor.affiliatedAuthor신재승-
dc.contributor.affiliatedAuthor신재승-
dc.contributor.affiliatedAuthor신현주-
dc.contributor.affiliatedAuthor신현주-
dc.contributor.affiliatedAuthor윤자경-
dc.contributor.affiliatedAuthor윤자경-
dc.contributor.affiliatedAuthor윤혜성-
dc.contributor.affiliatedAuthor윤혜성-
dc.contributor.affiliatedAuthor이미정-
dc.contributor.affiliatedAuthor이미정-
dc.contributor.affiliatedAuthor한경화-
dc.contributor.affiliatedAuthor한경화-
dc.citation.volume9-
dc.citation.number1-
dc.citation.startPage19420-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.9(1) : 19420, 2019-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
5. Research Institutes (연구소) > Others (기타) > 1. Journal Papers

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