Networks of Cardiovascular Digital Twins
Pioneering personalised cardiovascular care through interconnected digital twins
What is Pulmonary Arterial Hypertension?
Pulmonary Arterial Hypertension (PAH) is a type of pulmonary hypertension (PH)—a serious condition affecting the arteries in the lungs and the right side of the heart. In PAH, these arteries become narrowed or blocked, increasing the pressure and forcing the heart to work harder, which can eventually lead to heart failure.
Why focus on PAH?
It's a life-threatening condition with high mortality and frequent clinical worsening events. This makes it a critical area for improvement and a vital opportunity for the CVD-Net project to develop and rigorously test the forecasting capabilities of our digital twin technology.
What are the goals of CVD-Net?
CVD-Net is pioneering the future of cardiovascular care through innovative networked digital twins. By connecting personalised computational models of patients' hearts, we're creating a new paradigm for predicting, monitoring, and treating cardiovascular diseases.
Creating advanced computer models (digital twins) that represent individual patients' cardiovascular health and connecting them in a secure, networked virtual environment.
Using these digital twins to better track disease progression and predict future health events and treatment responses for Pulmonary Arterial Hypertension (PAH).
Enabling more timely, tailored, and effective healthcare decisions based on an individual's personalised digital twin.
Creating the underlying technology for connected digital twins, allowing them to learn from each other, making them more robust, scalable, and mitigating biases.
Creating a proof-of-principle digital twin care pathway within a healthcare pathway, paving the way for future adoption.
Partner Organisations and Funding
The Networks of Cardiovascular Digital Twins (CVD-Net) project is a collaboration between Imperial College London, the Alan Turing Institute, University of Sheffield, and University of Nottingham. The project is funded by the UKRI Engineering and Physical Sciences Research Council (EPSRC) via grant (EP/Z531297/1) awarded to Professor Steven Niederer.
