{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:27:19Z","timestamp":1791559639504,"version":"4.3.4"},"reference-count":23,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["JGH Open"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Aim<\/jats:title><jats:p>Gastric atrophy is a precancerous lesion. We aimed to clarify whether gastric atrophy determined by artificial intelligence (AI) correlates with the diagnosis made by expert endoscopists using several endoscopic classifications, the Operative Link on Gastritis Assessment (OLGA) classification based on histological findings, and genotypes associated with gastric atrophy and cancer.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Two hundred seventy <jats:italic>Helicobacter pylori<\/jats:italic>\u2010positive outpatients were enrolled. All patients' endoscopy data were retrospectively evaluated based on the Kimura\u2010Takemoto, modified Kyoto, and OLGA classifications. The AI\u2010trained neural network generated a continuous number between 0 and 1 for gastric atrophy. Nucleotide variance of some candidate genes was confirmed or selectively assessed for a variety of genotypes, including the <jats:italic>COX\u20102<\/jats:italic>1195, <jats:italic>IL\u20101\u03b2<\/jats:italic> 511, and <jats:italic>mPGES\u20101<\/jats:italic> genotypes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>There were significant correlations between determinations of gastric atrophy by AI and by expert endoscopists using not only the Kimura\u2010Takemoto classification (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001), but also the modified Kyoto classification (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.046 and <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001 for the two criteria). Moreover, there was a significant correlation with the OLGA classification (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.009). Nucleotide variance of the <jats:italic>COX\u20102<\/jats:italic>, <jats:italic>IL\u20101\u03b2<\/jats:italic>, and <jats:italic>mPGES\u20101<\/jats:italic>genes was not significantly associated with gastric atrophy determined by AI. The area under the curve values of the combinations of AI and the modified Kyoto classification (0.746) and AI and the OLGA classification (0.675) were higher than in AI alone (0.665).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Combinations of AI and the modified Kyoto classification or of AI and the OLGA classification could be useful tools for evaluating gastric atrophy in patients with <jats:italic>H<\/jats:italic>. <jats:italic>pylori<\/jats:italic> infection as the risk of gastric cancer.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jgh3.12810","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T12:18:36Z","timestamp":1661516316000},"page":"704-710","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Determination of gastric atrophy with artificial intelligence compared to the assessments of the modified Kyoto and <scp>OLGA<\/scp> classifications"],"prefix":"10.1002","volume":"6","author":[{"given":"Yasuhiro","family":"Kodaka","sequence":"first","affiliation":[{"name":"Division of Gastroenterology Nippon Medical School  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0502-8646","authenticated-orcid":false,"given":"Seiji","family":"Futagami","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Nippon Medical School  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshiyuki","family":"Watanabe","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Nippon Medical School  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoki","family":"Shichijo","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Oncology Osaka International Cancer Institute  Osaka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noriya","family":"Uedo","sequence":"additional","affiliation":[{"name":"Department of Gastrointestinal Oncology Osaka International Cancer Institute  Osaka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Aono","sequence":"additional","affiliation":[{"name":"AI Medical Service Inc.  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Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobue","family":"Ueki","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Nippon Medical School  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhei","family":"Agawa","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Nippon Medical School  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Yamawaki","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Nippon Medical School  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5558-6104","authenticated-orcid":false,"given":"Katsuhiko","family":"Iwakiri","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Nippon Medical School  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":[[2022,8,26]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.canlet.2016.05.033"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2017-314547"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2017.10.010"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2018.04.003"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10120-018-0793-2"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2018.07.037"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/den.13507"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2018.10.027"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/00365521.2019.1577486"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2036.2010.04277.x"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1440-1746.2003.03192.x"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/apt.12291"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2015-309252"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dld.2008.02.030"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/den.12286"},{"key":"e_1_2_7_19_1","article-title":"Application of convolutional neural networks for stratification of the degree of endoscopic atrophy on endoscopic images","volume":"7","author":"Shichijo S","year":"2019","journal-title":"United Eur. 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