Deep learning model for automated detection of Helicobacter pylori and intestinal metaplasia on gastric biopsy digital whole slide images.

OBJECTIVE

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&E) whole-slide images (WSIs), incorporating background histopathologic features.

METHODS

A total of 180 H&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-a Vision Transformer-based 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.

RESULTS

A total of 776 636 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.

CONCLUSIONS

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.