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Software development study demonstrates automated digital twin generation for type 1 diabetes via a web platform, suggesting improved clinical accessibility for in silico therapy testing.
BACKGROUND: ReplayBG is a recently released tool that acts as a digital twin (DT) of individuals with type 1 diabetes, learning glucose-insulin dynamics from real-world data and enabling users to test the effect of different therapeutic actions. However, its adoption remains limited by the technical expertise required to operate it. METHODS: To lower this barrier, we developed ReplayBG-web, a web-based interface built on ReplayBG that allows any user, without programming skills, to upload standardized CSV files and automatically generate DTs. RESULTS: ReplayBG-web enables users to interactively visualize original data and metrics, create new scenarios by modifying the dose and timing of insulin and meals, obtain the resulting alternative glucose profiles and related control metrics, and export results for downstream analysis. CONCLUSIONS: ReplayBG-web represents a step forward in filling the gap between research, clinical practice, self-learning, and potentially education, making digital twin technology for type 1 diabetes accessible to a wider range of users.
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Cossu et al. (2026) studied this question.
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