Cited 13 times in
Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis
DC Field | Value | Language |
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dc.contributor.author | 김성원 | - |
dc.contributor.author | 천재희 | - |
dc.date.accessioned | 2021-10-21T00:05:24Z | - |
dc.date.available | 2021-10-21T00:05:24Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 0815-9319 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/185371 | - |
dc.description.abstract | Background 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.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Blackwell Scientific Publications | - |
dc.relation.isPartOf | JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Jung Min Kim | - |
dc.contributor.googleauthor | Jun Gu Kang | - |
dc.contributor.googleauthor | Sungwon Kim | - |
dc.contributor.googleauthor | Jae Hee Cheon | - |
dc.identifier.doi | 10.1111/jgh.15433 | - |
dc.contributor.localId | A05309 | - |
dc.contributor.localId | A04030 | - |
dc.relation.journalcode | J01417 | - |
dc.identifier.eissn | 1440-1746 | - |
dc.identifier.pmid | 33554375 | - |
dc.identifier.url | https://onlinelibrary.wiley.com/doi/10.1111/jgh.15433 | - |
dc.subject.keyword | Behçet's disease | - |
dc.subject.keyword | Crohn's disease | - |
dc.subject.keyword | deep learning | - |
dc.subject.keyword | intestinal tuberculosis | - |
dc.contributor.alternativeName | Kim, Sungwon | - |
dc.contributor.affiliatedAuthor | 김성원 | - |
dc.contributor.affiliatedAuthor | 천재희 | - |
dc.citation.volume | 36 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 2141 | - |
dc.citation.endPage | 2148 | - |
dc.identifier.bibliographicCitation | JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY, Vol.36(8) : 2141-2148, 2021-08 | - |
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