{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T10:40:15Z","timestamp":1756723215178,"version":"3.44.0"},"reference-count":21,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T00:00:00Z","timestamp":1752883200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Gastro and Hepatol"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background and Aim<\/jats:title><jats:p>We aimed to develop a deep convolutional neural network (DCNN) that integrates features from multiple sites of the stomach to classify Hp infection status, distinguishing between uninfected, previously infected, and currently infected.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Ten deep learning architectures were employed to develop DCNN models using a training dataset comprising 3380 white\u2010light images collected from 676 subjects across eight centers. External validation was conducted with a separate dataset consisting of images from 126 individuals. External testing was subsequently performed to assess and compare the diagnostic efficacy between single\u2010site and multisite fusion DCNN models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among these models, the DCNN model using Wide\u2010ResNet emerged as the top performer, achieving a high accuracy of 68.11% (95% confidence interval [CI]: 63.36%\u201373.09%) with an area under the curve (AUC) of 75.06% (95% CI: 70.22%\u201380.24%) for noninfection, 69.18% (95% CI: 64.51%\u201374.03%) for past infection, and 77.04% (95% CI: 72.12%\u201382.39%) for current infection using images from a single site on the lesser gastric curvature. In comparison, the voting\u2010based multisite fusion DCNN model demonstrated superior accuracy (73.83%, 95% CI: 69.12%\u201378.65%) and AUC (77.51%, 95% CI: 72.89%\u201382.59%), particularly notable for noninfection and current infection. Additionally, the DCNN model exhibited heightened sensitivity, specificity, and precision compared to experienced endoscopists.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The DCNN model, crafted through a voting\u2010based multisite fusion, displayed stellar performance, excelling in the classification of Hp infection status into uninfected and currently infected.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jgh.70004","type":"journal-article","created":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T08:33:46Z","timestamp":1752914026000},"page":"2240-2247","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multisite Fusion\u2010Based Deep Convolutional Neural Network for Classification of <scp><i>Helicobacter pylori<\/i><\/scp> Infection Status Using Endoscopic Images: A Multicenter Study"],"prefix":"10.1111","volume":"40","author":[{"given":"Duwei","family":"Dai","sequence":"first","affiliation":[{"name":"Department of Gastroenterology The Second Affiliated Hospital of Xi'an Jiaotong University  Xi'an China"},{"name":"Institute of Medical Artificial Intelligence The Second Affiliated Hospital of Xi'an Jiaotong University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojing","family":"Quan","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology The Second Affiliated Hospital of Xi'an Jiaotong University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2808-551X","authenticated-orcid":false,"given":"Yueqin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology The Second Affiliated Hospital of Xi'an Jiaotong University  Xi'an 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