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Application of A Convolutional Neural Network in The Diagnosis of Gastric Mesenchymal Tumors on Endoscopic Ultrasonography Images
DC Field | Value | Language |
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dc.contributor.author | 박준철 | - |
dc.date.accessioned | 2021-09-29T00:34:48Z | - |
dc.date.available | 2021-09-29T00:34:48Z | - |
dc.date.issued | 2020-10 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/183927 | - |
dc.description.abstract | Background and aims: Endoscopic ultrasonography (EUS) is a useful diagnostic modality for evaluating gastric mesenchymal tumors; however, differentiating gastrointestinal stromal tumors (GISTs) from benign mesenchymal tumors such as leiomyomas and schwannomas remains challenging. For this reason, we developed a convolutional neural network computer-aided diagnosis (CNN-CAD) system that can analyze gastric mesenchymal tumors on EUS images. Methods: A total of 905 EUS images of gastric mesenchymal tumors (pathologically confirmed GIST, leiomyoma, and schwannoma) were used as a training dataset. Validation was performed using 212 EUS images of gastric mesenchymal tumors. This test dataset was interpreted by three experienced and three junior endoscopists. Results: The sensitivity, specificity, and accuracy of the CNN-CAD system for differentiating GISTs from non-GIST tumors were 83.0%, 75.5%, and 79.2%, respectively. Its diagnostic specificity and accuracy were significantly higher than those of two experienced and one junior endoscopists. In the further sequential analysis to differentiate leiomyoma from schwannoma in non-GIST tumors, the final diagnostic accuracy of the CNN-CAD system was 72.5%, which was significantly higher than that of two experienced and one junior endoscopists. Conclusions: Our CNN-CAD system showed high accuracy in diagnosing gastric mesenchymal tumors on EUS images. It may complement the current clinical practices in the EUS diagnosis of gastric mesenchymal tumors. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | MDPI AG | - |
dc.relation.isPartOf | JOURNAL OF CLINICAL MEDICINE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Application of A Convolutional Neural Network in The Diagnosis of Gastric Mesenchymal Tumors on Endoscopic Ultrasonography Images | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Yoon Ho Kim | - |
dc.contributor.googleauthor | Gwang Ha Kim | - |
dc.contributor.googleauthor | Kwang Baek Kim | - |
dc.contributor.googleauthor | Moon Won Lee | - |
dc.contributor.googleauthor | Bong Eun Lee | - |
dc.contributor.googleauthor | Dong Hoon Baek | - |
dc.contributor.googleauthor | Do Hoon Kim | - |
dc.contributor.googleauthor | Jun Chul Park | - |
dc.identifier.doi | 10.3390/jcm9103162 | - |
dc.contributor.localId | A01676 | - |
dc.relation.journalcode | J03556 | - |
dc.identifier.eissn | 2077-0383 | - |
dc.identifier.pmid | 33003602 | - |
dc.subject.keyword | artificial intelligence | - |
dc.subject.keyword | endoscopic ultrasonography | - |
dc.subject.keyword | gastrointestinal stromal tumor | - |
dc.subject.keyword | mesenchymal tumor | - |
dc.subject.keyword | stomach | - |
dc.contributor.alternativeName | Park, Jun Chul | - |
dc.contributor.affiliatedAuthor | 박준철 | - |
dc.citation.volume | 9 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 3162 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CLINICAL MEDICINE, Vol.9(10) : 3162, 2020-10 | - |
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