{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:31:44Z","timestamp":1790573504833,"version":"4.1.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation, Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop an automated detection tool for Helicobacter pylori (HP) microorganisms (HPOrg) and intestinal metaplasia (IM) identification on gastric biopsy specimens on hematoxylin and eosin (H&amp;E) whole-slide images (WSIs), incorporating background histopathologic features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 180 H&amp;E gastric biopsy WSIs, archived at the Department of Anatomical Pathology, Singapore General Hospital, were used to train, validate, and test (60:20:20) a decision support tool. Eighty WSIs displayed non-HP inflammation; 100 were annotated for HP-associated gastritis, HPOrg, and IM. A 2-stage model was employed\u2014a Vision Transformer\u2013based model filtered artifacts after stain normalization, and then a Graph Attention Network component aggregated patch-level features, giving a prediction for each of 6 tissue sections within each WSI, with a majority vote determining the final WSI prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 776\u2009636 patches were used for training\/validation and testing. The optimized model showed HPOrg classification (precision: 0.604, F1-score: 0.617, and top 10 micro F1-score: 0.714) and IM classification (precision: 0.905, F1-score: 0.861, and top 10 micro F1-score: 1.0). The macro average F1-score was 0.739, section-level precision was 0.981, and the F1-score was 0.945. The WSI-level precision achieved was 1.0, with a F1-score of 0.96.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We demonstrate a 2-stage model to detect HP and IM in gastric biopsy specimens, considering background inflammation, which more closely reflects real-world clinical diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ajcp\/aqaf110","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T11:26:14Z","timestamp":1756725974000},"page":"830-839","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning model for automated detection of\n                    <i>Helicobacter pylori<\/i>\n                    and intestinal metaplasia on gastric biopsy digital whole slide images"],"prefix":"10.1093","volume":"164","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6622-9447","authenticated-orcid":false,"given":"Li 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