Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.

中文翻译：

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代谢疾病研究中的数字双胞胎和人工智能


数字孪生技术正在成为慢性病管理中个性化医疗的变革范例。在本文中，我们探讨了数字孪生的概念和关键特征及其在慢性非传染性代谢疾病管理中的应用，重点是糖尿病案例研究。我们涵盖了各种类型的数字孪生模型，包括基于 ODE 的机械模型、数据驱动的 ML 算法以及结合了两种方法优势的混合建模策略。我们提供成功的案例研究，展示数字双胞胎在改善 T1D 和 T2D 患者血糖结果方面的潜力，并讨论将数字双胞胎研究应用转化为临床实践的好处和挑战。