Cited 6 times in
Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet)
DC Field | Value | Language |
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dc.contributor.author | 임동진 | - |
dc.date.accessioned | 2023-04-07T01:32:15Z | - |
dc.date.available | 2023-04-07T01:32:15Z | - |
dc.date.issued | 2022-12 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/193973 | - |
dc.description.abstract | Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase the accuracy of diagnosis of acute thoracic aortic dissection based on chest x-rays by applying deep learning techniques. In aggregate, 3,331 images, comprising 716 positive images and 2615 negative images, were collected from 3,331 patients. Residual neural network 18 was used to detect acute thoracic aortic dissection. The diagnostic accuracy of the ResNet18 was observed to be 90.20% with a precision of 75.00%, recall of 94.44%, and F1-score of 83.61%. Further research is required to improve diagnostic accuracy based on aorta segmentation. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Aorta | - |
dc.subject.MESH | Aortic Dissection* | - |
dc.subject.MESH | Dissection, Thoracic Aorta* | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Neural Networks, Computer | - |
dc.subject.MESH | Radiography, Thoracic / methods | - |
dc.title | Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet) | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Dong Keon Lee | - |
dc.contributor.googleauthor | Jin Hyuk Kim | - |
dc.contributor.googleauthor | Jaehoon Oh | - |
dc.contributor.googleauthor | Tae Hyun Kim | - |
dc.contributor.googleauthor | Myeong Seong Yoon | - |
dc.contributor.googleauthor | Dong Jin Im | - |
dc.contributor.googleauthor | Jae Ho Chung | - |
dc.contributor.googleauthor | Hayoung Byun | - |
dc.identifier.doi | 10.1038/s41598-022-26486-3 | - |
dc.contributor.localId | A03361 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 36536152 | - |
dc.contributor.alternativeName | Im, Dong Jin | - |
dc.contributor.affiliatedAuthor | 임동진 | - |
dc.citation.volume | 12 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 21884 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.12(1) : 21884, 2022-12 | - |
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