Realizing personalized prediction for the reversal of chronic hepatitis B-associated liver fibrosis through machine learning

Abstract

Although long-term antiviral therapy can reverse liver fibrosis in a considerable proportion of patients with chronic hepatitis B, treatment response differs markedly among individuals, and reliable methods for predicting who will benefit remain lacking. Conventional clinical tools such as liver stiffness measurement and the aspartate aminotransferase-to-platelet ratio index were originally dewsigned for cross-sectional staging rather than longitudinal prediction, and their ability to capture the dynamic process of fibrosis reversal is limited. In this opinion review, we discuss how machine learning (ML) may help address this unmet need. We focus on three areas where ML has shown early but encouraging results: Quantitative analysis of images using ML, integration of pathological and clinical data through multimodal models, and dynamic prediction of treatment response based on serial measurements during antiviral therapy. In our view, the greatest value of ML lies not in replacing existing assessment tools but in complementing them, particularly through the early identification of patients who are unlikely to achieve fibrosis reversal with antiviral therapy alone. However, we also caution that most current models have been developed from small, single-center retrospective cohorts, and their clinical adoption will require prospective multi-center validation, improved model interpretability, and adherence to standardized reporting frameworks.