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Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison
DC Field | Value | Language |
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dc.contributor.author | 강은애 | - |
dc.date.accessioned | 2023-06-02T00:47:15Z | - |
dc.date.available | 2023-06-02T00:47:15Z | - |
dc.date.issued | 2022-02 | - |
dc.identifier.issn | 0016-5107 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/194430 | - |
dc.description.abstract | Background and aims: Endoscopic differential diagnoses of gastric mucosal lesions (benign gastric ulcer, early gastric cancer [EGC], and advanced gastric cancer) remain challenging. We aimed to develop and validate convolutional neural network-based artificial intelligence (AI) models: lesion detection, differential diagnosis (AI-DDx), and invasion depth (AI-ID; pT1a vs pT1b among EGC) models. Methods: This study included 1366 consecutive patients with gastric mucosal lesions from 2 referral centers in Korea. One representative endoscopic image from each patient was used. Histologic diagnoses were set as the criterion standard. Performance of the AI-DDx (training/internal/external validation set, 1009/112/245) and AI-ID (training/internal/external validation set, 620/68/155) was compared with visual diagnoses by independent endoscopists (stratified by novice [<1 year of experience], intermediate [2-3 years of experience], and expert [>5 years of experience]) and EUS results, respectively. Results: The AI-DDx showed good diagnostic performance for both internal (area under the receiver operating characteristic curve [AUROC] = .86) and external validation (AUROC = .86). The performance of the AI-DDx was better than that of novice (AUROC = .82, P = .01) and intermediate endoscopists (AUROC = .84, P = .02) but was comparable with experts (AUROC = .89, P = .12) in the external validation set. The AI-ID showed a fair performance in both internal (AUROC = .78) and external validation sets (AUROC = .73), which were significantly better than EUS results performed by experts (internal validation, AUROC = .62; external validation, AUROC = .56; both P < .001). Conclusions: The AI-DDx was comparable with experts and outperformed novice and intermediate endoscopists for the differential diagnosis of gastric mucosal lesions. The AI-ID performed better than EUS for evaluation of invasion depth. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Mosby Yearbook | - |
dc.relation.isPartOf | GASTROINTESTINAL ENDOSCOPY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Area Under Curve | - |
dc.subject.MESH | Artificial Intelligence* | - |
dc.subject.MESH | Deep Learning* | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Neural Networks, Computer | - |
dc.subject.MESH | ROC Curve | - |
dc.title | Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Joon Yeul Nam | - |
dc.contributor.googleauthor | Hyung Jin Chung | - |
dc.contributor.googleauthor | Kyu Sung Choi | - |
dc.contributor.googleauthor | Hyuk Lee | - |
dc.contributor.googleauthor | Tae Jun Kim | - |
dc.contributor.googleauthor | Hosim Soh | - |
dc.contributor.googleauthor | Eun Ae Kang | - |
dc.contributor.googleauthor | Soo-Jeong Cho | - |
dc.contributor.googleauthor | Jong Chul Ye | - |
dc.contributor.googleauthor | Jong Pil Im | - |
dc.contributor.googleauthor | Sang Gyun Kim | - |
dc.contributor.googleauthor | Joo Sung Kim | - |
dc.contributor.googleauthor | Hyunsoo Chung | - |
dc.contributor.googleauthor | Jeong-Hoon Lee | - |
dc.identifier.doi | 10.1016/j.gie.2021.08.022 | - |
dc.contributor.localId | A05966 | - |
dc.relation.journalcode | J00920 | - |
dc.identifier.eissn | 1097-6779 | - |
dc.identifier.pmid | 34492271 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0016510721016138 | - |
dc.contributor.alternativeName | Kang, Eun Ae | - |
dc.contributor.affiliatedAuthor | 강은애 | - |
dc.citation.volume | 95 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 258 | - |
dc.citation.endPage | 268.e10 | - |
dc.identifier.bibliographicCitation | GASTROINTESTINAL ENDOSCOPY, Vol.95(2) : 258-268.e10, 2022-02 | - |
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