Research Article: A machine-learning model for staging autoimmune gastritis based on endoscopic and serological features
Abstract: Introduction:
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.
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%
Autoimmune gastritis (AIG) is a chronic, organ-specific autoimmune disease characterized by T-cell–mediated destruction of gastric parietal cells, achlorhydria, and progressive corpus-fundic atrophy ( 1 – 3 ). It predominantly affects middle-aged and older women and is frequently associated with other autoimmune conditions, including autoimmune thyroid disease and type 1 diabetes ( 1 ). Because the clinical presentation is insidious, a substantial proportion of patients are diagnosed only after the disease has…
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