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Digital Twins in Type 1 Diabetes: A Systematic Review
Giacomo Cappon1, Andrea Facchinetti1
1Department of Information Engineering, University of Padova, Padova, Italy.
Journal of Diabetes Science and Technology
|June 18, 2024
Summary
Digital twins offer personalized therapies for type 1 diabetes (T1D) by simulating glucose-insulin metabolism. Current T1D digital twin methodologies focus on glucose concentration, not comprehensive patient replicas.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Endocrinology
Background:
- Digital twins, virtual patient representations, are emerging in medicine for personalized therapies.
- Their application in type 1 diabetes (T1D) management is currently limited.
- This review systematically analyzes existing digital twins for T1D.
Purpose of the Study:
- To analyze the structure, operating conditions, and characteristics of digital twins developed for T1D.
- To compare methodologies focusing on glucose-insulin metabolism simulation.
- To identify limitations and future directions for T1D digital twins.
Main Methods:
- Systematic literature review of digital twins for T1D.
- Searched publications up to March 2024, identifying 8 eligible studies.
- Analysis of operating principles, twinning procedures, validation, and practical adoption factors.
Main Results:
- All 8 reviewed methodologies focus on simulating glucose concentration, not comprehensive multi-scale patient replicas.
- Key aspects analyzed include mathematical models, data requirements, and physical activity inclusion.
- Variations exist in validation, assessment, and open-source availability.
Conclusions:
- Current T1D digital twins primarily simulate glucose-insulin dynamics for therapeutic intervention.
- They are not yet comprehensive virtual replicas of individuals with T1D.
- Further development is needed to enhance their scope and practical adoption in diabetes care.
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