发表机构
University of Florida（佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出生理学驱动的肝脏数字孪生模型HEPATWIN，整合肝脏生理机制与患者特异性输入，经校准后可模拟肝脏疾病进展，能生成临床可用生物标志物，支持NASH检测，为肝脏健康监测提供新途径。
AI 中文摘要
我们提出了一种针对人体肝脏的生理学驱动数字孪生模型，旨在对肝功能及早期疾病进展进行纵向模拟。该模型名为HEPATWIN，在统一的系统级框架中整合了碳水化合物、脂质、蛋白质代谢，胆红素结合、胆汁生成及解毒等关键肝脏过程，以生成临床可观测的生物标志物轨迹。与纯数据驱动方法不同，HEPATWIN纳入了肝脏生理学的机理性表征以及饮食、活动、基线生物标志物等患者特异性输入，用于模拟疾病随时间的演化。为确保与临床进展模式一致，我们引入了阶段转换驱动的校准机制，使模拟输出与非酒精性脂肪性肝病（NAFLD）、肝纤维化、肝硬化等疾病阶段的人群水平生物标志物分布对齐。使用NIDDK NAFLD数据集进行的验证表明，HEPATWIN生成的纵向生物标志物估计值处于临床可接受范围内，且能预测多年跨度的轨迹；此外，模拟生物标志物保留了足够的临床信号，支持后续非酒精性脂肪性肝炎（NASH）检测，性能与使用真实实验室数据的模型相当。这些结果凸显了生理学驱动数字孪生在个性化、无创诊断及器官健康预测（尤其是肝脏健康监测）方面的潜力。
英文摘要
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.
CommentsThis paper is under review at IEEE Journal of Biomedical and Health Informatics (JBHI)
