构建人体器官数字孪生，实现个性化医疗的实时模拟与预测
Building Digital Twins of Different Human Organs for Personalized Healthcare
- 分两步构建：先还原患者特异性解剖结构，再模拟细胞到器官的多尺度生理过程
- 人工智能助力提升模型精度与个性化水平，尤其物理信息神经网络表现突出
- 适合医学工程、精准医疗研究者，推动从单器官到全身数字孪生的跨越
数字孪生是物理实体的虚拟副本，有望通过实时模拟和预测人体生理状态，推动个性化医疗发展。将这一范式从工程领域迁移至生物医学，面临解剖变异、多尺度生物过程及多物理场融合等深层挑战。本文系统综述了人体器官数字孪生的构建方法，按照解剖孪生（捕捉个体化几何与结构）与功能孪生（模拟从细胞到器官水平的多尺度生理）的流程进行梳理。按器官特性与技术范式分类，重点强调多尺度与多物理场整合。特别关注人工智能，尤其是物理信息驱动的AI，在提升模型保真度、可扩展性与个性化方面的关键作用。同时讨论临床验证与转化路径中的核心挑战。本研究不仅为突破单一器官孪生的瓶颈指明方向，也展望了互联多器官数字孪生在全身体精密医疗中的广阔前景。
原文摘要 · Abstract (English)
Digital twins are virtual replicas of physical entities and are poised to transform personalized medicine through the real-time simulation and prediction of human physiology. Translating this paradigm from engineering to biomedicine requires overcoming profound challenges, including anatomical variability, multi-scale biological processes, and the integration of multi-physics phenomena. This survey systematically reviews methodologies for building digital twins of human organs, structured around a pipeline decoupled into anatomical twinning (capturing patient-specific geometry and structure) and functional twinning (simulating multi-scale physiology from cellular to organ-level function). We categorize approaches both by organ-specific properties and by technical paradigm, with particular emphasis on multi-scale and multi-physics integration. A key focus is the role of artificial intelligence (AI), especially physics-informed AI, in enhancing model fidelity, scalability, and personalization. Furthermore, we discuss the critical challenges of clinical validation and translational pathways. This study not only charts a roadmap for overcoming current bottlenecks in single-organ twins but also outlines the promising, albeit ambitious, future of interconnected multi-organ digital twins for whole-body precision healthcare.
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