DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nam, Joon Yeul | ko |
dc.contributor.author | Chung, Hyung Jin | ko |
dc.contributor.author | Choi, Kyu Sung | ko |
dc.contributor.author | Lee, Hyuk | ko |
dc.contributor.author | Kim, Tae Jun | ko |
dc.contributor.author | Soh, Hosim | ko |
dc.contributor.author | Kang, Eun Ae | ko |
dc.contributor.author | Cho, Soo-Jeong | ko |
dc.contributor.author | Ye, Jong Chul | ko |
dc.contributor.author | Im, Jong Pil | ko |
dc.contributor.author | Kim, Sang Gyun | ko |
dc.contributor.author | Kim, Joo Sung | ko |
dc.contributor.author | Chung, Hyunsoo | ko |
dc.contributor.author | Lee, Jeong-Hoon | ko |
dc.date.accessioned | 2022-02-08T06:41:44Z | - |
dc.date.available | 2022-02-08T06:41:44Z | - |
dc.date.created | 2022-02-08 | - |
dc.date.created | 2022-02-08 | - |
dc.date.created | 2022-02-08 | - |
dc.date.created | 2022-02-08 | - |
dc.date.created | 2022-02-08 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.citation | GASTROINTESTINAL ENDOSCOPY, v.95, no.2, pp.258 - + | - |
dc.identifier.issn | 0016-5107 | - |
dc.identifier.uri | http://hdl.handle.net/10203/292104 | - |
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.language | English | - |
dc.publisher | MOSBY-ELSEVIER | - |
dc.title | Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison | - |
dc.type | Article | - |
dc.identifier.wosid | 000743747500008 | - |
dc.identifier.scopusid | 2-s2.0-85118356475 | - |
dc.type.rims | ART | - |
dc.citation.volume | 95 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 258 | - |
dc.citation.endingpage | + | - |
dc.citation.publicationname | GASTROINTESTINAL ENDOSCOPY | - |
dc.identifier.doi | 10.1016/j.gie.2021.08.022 | - |
dc.contributor.localauthor | Ye, Jong Chul | - |
dc.contributor.nonIdAuthor | Nam, Joon Yeul | - |
dc.contributor.nonIdAuthor | Choi, Kyu Sung | - |
dc.contributor.nonIdAuthor | Lee, Hyuk | - |
dc.contributor.nonIdAuthor | Kim, Tae Jun | - |
dc.contributor.nonIdAuthor | Soh, Hosim | - |
dc.contributor.nonIdAuthor | Kang, Eun Ae | - |
dc.contributor.nonIdAuthor | Cho, Soo-Jeong | - |
dc.contributor.nonIdAuthor | Im, Jong Pil | - |
dc.contributor.nonIdAuthor | Kim, Sang Gyun | - |
dc.contributor.nonIdAuthor | Kim, Joo Sung | - |
dc.contributor.nonIdAuthor | Chung, Hyunsoo | - |
dc.contributor.nonIdAuthor | Lee, Jeong-Hoon | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | ARTIFICIAL-INTELLIGENCE | - |
dc.subject.keywordPlus | CANCER | - |
dc.subject.keywordPlus | ULTRASONOGRAPHY | - |
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