{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T20:14:37Z","timestamp":1790367277675,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Gastro and Hepatol"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aim<\/jats:title>\n                    <jats:p>Magnifying endoscopy with narrow\u2010band imaging (ME\u2010NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME\u2010NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI\u2010assisted CNN computer\u2010aided diagnosis (CAD) system, based on ME\u2010NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI\u2010assisted CNN\u2010CAD system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The AI\u2010assisted CNN\u2010CAD system (ResNet50) was trained and validated on a dataset of 5574 ME\u2010NBI images (3797 EGCs, 1777 non\u2010cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME\u2010NBI images (1430 EGCs, 870 non\u2010cancerous mucosa and lesions) was assessed using the AI\u2010assisted CNN\u2010CAD system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The AI\u2010assisted CNN\u2010CAD system required 60\u00a0s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low\u2010quality or of superficially depressed and intestinal\u2010type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The AI\u2010assisted CNN\u2010CAD system for ME\u2010NBI diagnosis of EGC could process many stored ME\u2010NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME\u2010NBI diagnosis of EGC in practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgh.15190","type":"journal-article","created":{"date-parts":[[2020,7,18]],"date-time":"2020-07-18T01:29:40Z","timestamp":1595035780000},"page":"482-489","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":142,"title":["Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow\u2010band imaging"],"prefix":"10.1111","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5370-1009","authenticated-orcid":false,"given":"Hiroya","family":"Ueyama","sequence":"first","affiliation":[{"name":"Department of Gastroenterology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuke","family":"Kato","sequence":"additional","affiliation":[{"name":"AI Medical Service Inc.  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoichi","family":"Akazawa","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noboru","family":"Yatagai","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Komori","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsutomu","family":"Takeda","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kohei","family":"Matsumoto","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kumiko","family":"Ueda","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenshi","family":"Matsumoto","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariko","family":"Hojo","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Human Pathology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akihito","family":"Nagahara","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Juntendo University School of Medicine  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomohiro","family":"Tada","sequence":"additional","affiliation":[{"name":"AI Medical Service Inc.  Tokyo Japan"},{"name":"Tada Tomohiro Institute of Gastroenterology and Proctology  Saitama Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,7,28]]},"reference":[{"key":"e_1_2_5_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.28065"},{"key":"e_1_2_5_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10043-003-0211-8"},{"key":"e_1_2_5_4_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0029-1214594"},{"key":"e_1_2_5_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cgh.2007.02.034"},{"key":"e_1_2_5_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2010.04.041"},{"key":"e_1_2_5_7_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2011.08.007"},{"key":"e_1_2_5_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10120-013-0332-0"},{"key":"e_1_2_5_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2013.07.008"},{"key":"e_1_2_5_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/den.12638"},{"key":"e_1_2_5_11_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0043-111888"},{"key":"e_1_2_5_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_5_13_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.14585"},{"key":"e_1_2_5_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dld.2007.03.004"},{"key":"e_1_2_5_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2018.07.037"},{"key":"e_1_2_5_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/jgh.12149"},{"key":"e_1_2_5_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2017.11.029"},{"key":"e_1_2_5_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10120-018-0793-2"},{"key":"e_1_2_5_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2017.10.014"},{"key":"e_1_2_5_20_1","first-page":"462","article-title":"Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging\u2010bright and linked color imaging: a singlecenter prospective study","volume":"31","author":"Nakashima H","year":"2018","journal-title":"Ann. 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