| Title: | The cardiovascular digital twin of the adult population: defining the population ranges. |
| Lead Institution: | King's College London |
| Principal investigator: |
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About
Improving healthcare systems in the UK in a period of aging population and tightening financial constraints mandates a shift towards efficient, personalised and preventive management of disease, and towards improved healthcare processes. In this context, we have the vision of Digital Twin (DT) technologies providing a pathway to more efficient, data-driven, personalised, and preventive healthcare. A DT is a computational replica of the patient, continuously updated to inform decisions, and equipped with unprecedented computational ability to support human decision making based on both data and models. Our specific aim towards this vision is to build the DT of the cardiovascular system of the UKBB cohort. In this way, we will create the benchmark of model parameters that will become the reference for future studies. This project has a duration of 3 years, has already secured support from the Wellcome Trust for both analysing the data and making the analysis tools available for any researcher in the world. Our results will provide any researcher with the ability to build personalised computational models of the cardiovascular system, and thus unlock the potential of DT's to inform clinical diagnosis, prognosis and therapy planning.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 16169 | Cardiac digital twins at scale from MRI: Open tools and representative models from ~ 55000 UK Biobank participants | Devran Ugurlu (+12) | 2025 | PLOS ONE |
| 15330 | Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization | Shuang Qian (+19) | 2025 | Nature Cardiovascular Research |
Enabling scientific discoveries that improve human health