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Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis

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dc.contributor.author김성원-
dc.contributor.author천재희-
dc.date.accessioned2021-10-21T00:05:24Z-
dc.date.available2021-10-21T00:05:24Z-
dc.date.issued2021-08-
dc.identifier.issn0815-9319-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/185371-
dc.description.abstractBackground and aim: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic findings. This study aimed to develop a deep-learning algorithm that performs differential diagnosis between intestinal Behçet's disease (BD), Crohn's disease (CD), and intestinal tuberculosis (ITB) using colonoscopy images. Methods: The typical pattern for each disease was defined as a typical image. We implemented a convolutional neural network (CNN) using Pytorch and visualized a deep-learning model through Gradient-weighted Class Activation Mapping. The performance of the algorithm was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: A total of 6617 colonoscopy images of 211 CD, 299 intestinal BD, and 217 ITB patients were used. The accuracy of the algorithm for discriminating the three diseases (all-images: 65.15% vs typical images: 72.01%, P = 0.024) and discriminating between intestinal BD and CD (all-images: 78.15% vs typical images: 85.62%, P = 0.010) was significantly different between all-images and typical images. The CNN clearly differentiated colonoscopy images of the diseases (AUROC from 0.7846 to 0.8586). Algorithmic prediction AUROC for typical images ranged from 0.8211 to 0.9360. Conclusion: This study found that a deep-learning model can discriminate between colonoscopy images of intestinal BD, CD, and ITB. In particular, the algorithm demonstrated superior discrimination ability for typical images. This approach presents a beneficial method for the differential diagnosis of the diseases.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherBlackwell Scientific Publications-
dc.relation.isPartOfJOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDeep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorJung Min Kim-
dc.contributor.googleauthorJun Gu Kang-
dc.contributor.googleauthorSungwon Kim-
dc.contributor.googleauthorJae Hee Cheon-
dc.identifier.doi10.1111/jgh.15433-
dc.contributor.localIdA05309-
dc.contributor.localIdA04030-
dc.relation.journalcodeJ01417-
dc.identifier.eissn1440-1746-
dc.identifier.pmid33554375-
dc.identifier.urlhttps://onlinelibrary.wiley.com/doi/10.1111/jgh.15433-
dc.subject.keywordBehçet's disease-
dc.subject.keywordCrohn's disease-
dc.subject.keyworddeep learning-
dc.subject.keywordintestinal tuberculosis-
dc.contributor.alternativeNameKim, Sungwon-
dc.contributor.affiliatedAuthor김성원-
dc.contributor.affiliatedAuthor천재희-
dc.citation.volume36-
dc.citation.number8-
dc.citation.startPage2141-
dc.citation.endPage2148-
dc.identifier.bibliographicCitationJOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY, Vol.36(8) : 2141-2148, 2021-08-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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