A Physiology-Informed Digital Twin Framework for Simulating Liver Health Progression

2026-08-14

AI Summary

This study addresses the lack of precise personalized models for longitudinal simulation and early prediction of liver disease by proposing HEPATWIN, a physiology-driven digital twin. Integrating metabolic mechanisms with patient data, this model innovatively employs a stage-transition-driven calibration mechanism to achieve personalized longitudinal simulation of disease progression. Experimental results demonstrate that biomarker prediction accuracy falls within clinically acceptable ranges, supporting non-invasive NASH detection with performance comparable to models trained on real-world data. By effectively enabling personalized non-invasive diagnosis and long-term health forecasting, this work establishes a novel paradigm for the precision management of liver disease.

Abstract

We present a physiology-informed digital twin of the human liver designed for longitudinal simulation of liver function and early-stage disease progression. The model, referred to as HEPATWIN, integrates key hepatic processes, including carbohydrate, lipid, and protein metabolism, bilirubin conjugation, bile production, and detoxification, within a unified systems-level framework to generate clinically observable biomarker trajectories. Unlike purely data-driven approaches, HEPATWIN incorporates mechanistic representations of liver physiology and patient-specific inputs such as diet, activity, and baseline biomarkers to simulate disease evolution over time. To ensure consistency with clinical progression patterns, we introduce a stage-transition-driven calibration mechanism that aligns simulated outputs with population-level biomarker distributions across disease stages, including NAFLD, fibrosis, and cirrhosis. Validation using the NIDDK NAFLD dataset demonstrates that HEPATWIN produces longitudinal biomarker estimates within clinically acceptable ranges and can forecast trajectories over multi-year horizons. Furthermore, simulated biomarkers retain sufficient clinical signal to support downstream NASH detection with competitive performance relative to models using ground-truth laboratory data. These results highlight the potential of physiology-informed digital twins for personalized, non-invasive diagnosis and prediction of organ health in general and liver health monitoring in particular.

Problem

• Digital Twin
• Liver Health
• Disease Progression
• Longitudinal Simulation
• Personalized Medicine

Innovation

• Physiology-Informed Digital Twin
• Stage-Transition-Driven Calibration
• Longitudinal Simulation
• HEPATWIN
• Personalized Liver Health Monitoring