{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:58:28Z","timestamp":1785308308452,"version":"3.55.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Autoimmune gastritis (AIG) lacks a unified, practical staging system. The AIG-atrophic stage (AIG-AS) scheme, based on the proportional area of remnant oxyntic mucosa (ROM), is promising but observer-dependent. We developed machine-learning models to stage AIG using endoscopic and serological features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This single-center cross-sectional study enrolled 203 patients with AIG confirmed by integrated endoscopic, histological, and serological criteria between December 2023 and December 2025. White-light endoscopy (WLE), magnifying endoscopy with narrow-band imaging (ME-NBI), and a serum panel were collected. Patients were classified under AIG-AS as Stage 1 (50%&amp;lt;ROM \u2264 100%), Stage 2 (10%&amp;lt;ROM \u2264 50%), or Stage 3 (ROM \u2264 10%), and additionally as early-to-intermediate (Stages 1\u00a0+\u00a02) versus advanced (Stage 3). Seven algorithms\u2014LASSO logistic regression, elastic net, random forest, XGBoost, support vector machine, gradient boosting, and stacking\u2014were trained on five feature sets using stratified 5-fold nested cross-validation, with SMOTE applied only within training folds. SHAP analysis provided interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Stages 1, 2, and 3 included 87 (42.9%), 36 (17.7%), and 80 (39.4%) patients. The combined endoscopy+serology random forest achieved the highest three-class macro-AUC (0.878\u00a0\u00b1\u00a00.051; accuracy 74.9%), surpassing WLE stacking (0.853\u00a0\u00b1\u00a00.056) and serology-only models (\u22640.693). For binary staging, the WLE+ME-NBI random forest reached an AUC of 0.913\u00a0\u00b1\u00a00.046. Loss of gastric folds, gastrin-17, pepsinogen I, crypt-opening grade, and cast-off skin appearance were the dominant predictors. Barrett esophagus prevalence was 66.8%, with long-segment Barrett esophagus 3.3-fold more common in advanced AIG than in earlier disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Multi-modal machine learning enables accurate, interpretable AIG staging. Endoscopic features outperform serology, and combined models offer the best three-class performance. The high prevalence of Barrett esophagus supports routine esophageal surveillance in AIG.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1883686","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T05:58:36Z","timestamp":1785304716000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A machine-learning model for staging autoimmune gastritis based on endoscopic and serological features"],"prefix":"10.3389","volume":"17","author":[{"given":"Jingna","family":"Tao","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linghan","family":"Meng","sequence":"additional","affiliation":[{"name":"Guang\u2019anmen Hospital, China Academy of Chinese Medical Sciences","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liju","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese 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