{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T19:24:52Z","timestamp":1789500292070,"version":"build-2803163510"},"reference-count":38,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["JGH Open"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Aims<\/jats:title><jats:p>We aimed to assess the diagnostic potential of deep convolutional neural networks (DCNNs) for detecting <jats:italic>Helicobacter pylori<\/jats:italic> infection in patients who underwent esophagogastroduodenoscopy and <jats:italic>Campylobacter<\/jats:italic>\u2010like organism tests.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We categorized a total of 13,071 images of various gastric sub\u2010areas and employed five pretrained DCNN architectures: ResNet\u2010101, Xception, Inception\u2010v3, InceptionResnet\u2010v2, and DenseNet\u2010201. Additionally, we created an ensemble model by combining the output probabilities of the best models. We used images of different sub\u2010areas of the stomach for training and evaluated the performance of our models. The diagnostic metrics assessed included area under the curve (AUC), specificity, accuracy, positive predictive value, and negative predictive value.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>When training included images from all sub\u2010areas of the stomach, our ensemble model demonstrated the highest AUC (0.867), with specificity at 78.44%, accuracy at 80.28%, positive predictive value at 82.66%, and negative predictive value at 77.37%. Significant differences were observed in AUC between the ensemble model and the individual DCNN models. When training utilized images from each sub\u2010area separately, the AUC values for the antrum, cardia and fundus, lower body greater curvature and lesser curvature, and upper body greater curvature and lesser curvature regions were 0.842, 0.826, 0.718, and 0.858, respectively, when the ensemble model was used.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our study demonstrates that the DCNN model, designed for automated image analysis, holds promise for the evaluation and diagnosis of <jats:italic>Helicobacter pylori<\/jats:italic> infection.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jgh3.12995","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T04:59:46Z","timestamp":1699937986000},"page":"875-883","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Diagnostic usefulness of deep learning methods for <i>Helicobacter pylori<\/i> infection using esophagogastroduodenoscopy images"],"prefix":"10.1002","volume":"7","author":[{"given":"Daesung","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Healthcare Information Technology Inje University  Gimhae Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kayoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Family medicine, Busan Paik Hospital Inje University College of Medicine  Busan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5526-9707","authenticated-orcid":false,"given":"Jinseung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Family medicine, Busan Paik Hospital Inje University College of Medicine  Busan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"e_1_2_7_2_1","first-page":"299","article-title":"Helicobacter pylori in developing countries. 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