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Diagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists

DC FieldValueLanguage
dc.contributor.author곽진영-
dc.contributor.author김은경-
dc.contributor.author문희정-
dc.contributor.author박영진-
dc.contributor.author윤정현-
dc.contributor.author한경화-
dc.date.accessioned2020-02-11T06:23:04Z-
dc.date.available2020-02-11T06:23:04Z-
dc.date.issued2019-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/174655-
dc.description.abstractComputer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US). We aimed to develop a deep learning-based US CAD system (dCAD) for the diagnosis of thyroid nodules and compare its performance with those of a support vector machine (SVM)-based US CAD system (sCAD) and radiologists. dCAD was developed by using US images of 4919 thyroid nodules from three institutions. Its diagnostic performance was prospectively evaluated between June 2016 and February 2017 in 286 nodules, and was compared with those of sCAD and radiologists, using logistic regression with the generalized estimating equation. Subgroup analyses were performed according to experience level and separately for small thyroid nodules 1-2 cm. There was no difference in overall sensitivity, specificity, positive predictive value (PPV), negative predictive value and accuracy (all p > 0.05) between radiologists and dCAD. Radiologists and dCAD showed higher specificity, PPV, and accuracy than sCAD (all p < 0.001). In small nodules, experienced radiologists showed higher specificity, PPV and accuracy than sCAD (all p < 0.05). In conclusion, dCAD showed overall comparable diagnostic performance with radiologists and assessed thyroid nodules more effectively than sCAD, without loss of sensitivity.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDiagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorVivian Y. Park-
dc.contributor.googleauthorKyunghwa Han-
dc.contributor.googleauthorYeong Kyeong Seong-
dc.contributor.googleauthorMoon Ho Park-
dc.contributor.googleauthorEun-Kyung Kim-
dc.contributor.googleauthorHee Jung Moon-
dc.contributor.googleauthorJung Hyun Yoon-
dc.contributor.googleauthorJin Young Kwak-
dc.identifier.doi10.1038/s41598-019-54434-1-
dc.contributor.localIdA00182-
dc.contributor.localIdA00801-
dc.contributor.localIdA00801-
dc.contributor.localIdA01397-
dc.contributor.localIdA01397-
dc.contributor.localIdA01572-
dc.contributor.localIdA01572-
dc.contributor.localIdA02595-
dc.contributor.localIdA02595-
dc.contributor.localIdA04267-
dc.contributor.localIdA04267-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid31780753-
dc.contributor.alternativeNameKwak, Jin Young-
dc.contributor.affiliatedAuthor곽진영-
dc.contributor.affiliatedAuthor김은경-
dc.contributor.affiliatedAuthor김은경-
dc.contributor.affiliatedAuthor문희정-
dc.contributor.affiliatedAuthor문희정-
dc.contributor.affiliatedAuthor박영진-
dc.contributor.affiliatedAuthor박영진-
dc.contributor.affiliatedAuthor윤정현-
dc.contributor.affiliatedAuthor윤정현-
dc.contributor.affiliatedAuthor한경화-
dc.contributor.affiliatedAuthor한경화-
dc.citation.volume9-
dc.citation.number1-
dc.citation.startPage17843-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.9(1) : 17843, 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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