JMIR diabetes
Data, Process, and Data-Driven Representations of Digital Twins in Diabetes: Scoping Review
By Cinar B, van den Boom L and Maleshkova M.
Abstract
Background
Diabetes is a chronic metabolic condition characterized by impaired blood glucose regulation. It is often linked to serious health complications and comorbidities that significantly affect quality of life, requiring effective management, continuous monitoring, and advanced data analytics. Notably, tailored diabetes management can be enhanced by digital twins (DTs), which serve as adaptive digital representations of patients, using clinical, physiological, and lifestyle data.
Objective
This review explores diabetes-related DTs by examining their patient representation levels. We aim to synthesize the current state of the art and outline the foundations of a holistic, multilevel, multifunctional personalized DT for diabetes management.
Methods
We investigate requirements for a personalized holistic DT and classify existing approaches into three representation levels: (1) data representation, involving structured, context-aware, and AI-ready data architectures that support data analysis, enable semantic interoperability, relationship extraction, and real-time bidirectional data exchange between patient and virtual replica. (2) Process representation, primarily based on mechanistic models simulating glucose-insulin-meal and exercise-glucose dynamics. (3) Data-driven representation, focusing on individualization through predictive modeling of disease onset, adverse events, and the generation of explainable, personalized recommendations. The literature is synthesized to provide a holistic, multilevel perspective on DTs, and to identify research gaps.