{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T03:41:08Z","timestamp":1789962068855,"version":"4.0.1"},"reference-count":50,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Science Progress"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Training and validating vision transformer-based endoscopic assisted detection models for chronic atrophic gastritis (CAG) to assist endoscopists in detecting and localizing atrophic lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective study, gastroscopy images stored in the endoscopy center were collected between June 2019 and March 2023. On the basis of pathological findings, the images were manually classified into CAG and chronic nonatrophic gastritis (CNAG) using Labelme software, and the atrophic areas were further manually annotated in the CAG images. Furthermore, the anatomical structures were meticulously documented on the CNAG images. The labeled images were subsequently employed to train the Swin transformer and SSFormer for the task of detecting the anatomical structures of the stomach, CAG and atrophic lesion regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The test results revealed that the trained Swin transformer model had an accuracy of 0.98 in recognizing the anatomical structures of the stomach (gastric sinus, stomach angle, lesser curvature, cardia fundus, and greater curvature). Moreover, the accuracy, specificity, and sensitivity of the model in recognizing the CAG and CNAG images were 0.91, 0.95, and 0.86, respectively, which were significantly superior to those of the junior endoscopists who participated in the test (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u2009.05). In addition, the test results suggested that the trained SSFormer model had a similar ability to segment lesions as the senior endoscopist did, with the overlap of atrophic lesion regions indicated by both exceeding 0.90.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this study, a set of vision models was trained to identify not only CAG and intragastric structures but also the extent of atrophy. The application of these models to the diagnosis of CAG is expected to increase the accuracy of this process.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00368504251381972","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T04:46:00Z","timestamp":1758602760000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Constructing and validating vision transformer-based assisted detection models for atrophic gastritis: A retrospective study"],"prefix":"10.1177","volume":"108","author":[{"given":"Hu","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Xuzhou Municipal Hospital affiliated with Xuzhou Medical University (Xuzhou First People's Hospital), Xuzhou, Jiangsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of 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