Artificial Intelligence Model for Accurate Grading and Staging of Chronic Hepatitis B: Development and Validation

Abstract Background and aims: Chronic hepatitis B (CHB) is a global challenge, with histological assessment affected by observer variability. We aim to develop and validate BJ-HepaGS, an AI model for consistent evaluation. Methods: BJ-HepaGS was developed using hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) from CHB patients across multiple hospitals. Model performance was validated using area under the curve (AUC) and intraclass correlation coefficient (ICC) metrics. Results: The BJ-HepaGS trained on 673 WSIs (627,703 patches) and validated on independent (300 H&E-WSIs, 331,037 patches) and paired cohorts (n = 100 H&E-WSIs, 52,271 patches). The independent set achieved area under the curve (AUC) values of 0.91–0.98 for grading and 0.85–0.91 for staging, with strong consistency versus expert consensus (ICC = 0.824 and 0.681, respectively). BJ-HepaGS distinguished fibrosis stage F0-1 vs. F2-4 (87.6% accuracy) and cirrhosis (F0-3 vs. F4; 86.0% accuracy), and reliably assessed inflammation improvement (p = 0.885) and fibrosis regression (p = 0.388) in pre- and post-treatment paired samples. With the assistance of AI, the consistency between senior and junior expert interpretations on inflammation and fibrosis were significantly enhanced (both p < 0.001). Conclusions: BJ-HepaGS addresses a key gap in CHB care by providing reproducible, objective histopathological interpretation, supporting standardized diagnosis and improved clinical management.