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Updated: Aug 16, 2026
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Comprehensive and reproducible grading of the Updated Sydney System in gastric biopsies using artificial
Ho Heon Kim1,2, Won Chan Jeong1, Yuri Hwang1
1AI R&D Center, Seegene Medical Foundation, Seoul, Korea.
Aims:
The Updated Sydney System is the most widely used framework for histological grading of gastritis, but complete grading in routine gastric biopsies is time-consuming and subject to interobserver variability. We developed an artificial intelligence (AI) system to support comprehensive and reproducible grading of all five Updated Sydney attributes in gastric biopsies.
Methods And Results:
We developed SydneyMTL, a weakly supervised multi-task multiple instance learning framework trained on 50,765 whole-slide images from routine practice. The model jointly predicts mononuclear cell infiltration, neutrophil activity, glandular atrophy, intestinal metaplasia and Helicobacter pylori density, with atrophy including a separate not applicable category when muscularis mucosae is absent. Performance was assessed against routine labels from 24 board-certified pathologists and against a consensus-adjudicated Golden dataset. SydneyMTL achieved a mean lenient accuracy of 89.1% across the 24 pathologists, with >80% agreement for 21 of 24 readers on the retrospective dataset. In a two-reader randomized crossover study, AI assistance improved interobserver agreement and reduced review time by 34.2% for complete Updated Sydney grading.
Conclusions:
AI can support more reproducible and efficient grading of Updated Sydney histological attributes in gastric biopsies. By combining multi-pathologist validation, consensus-based evaluation and explicit handling of non-assessable atrophy, SydneyMTL provides a clinically realistic decision-support approach for routine digital pathology.
