The digital twin is an in-silico representation of a physical asset, entity or process. The twin has three key components. First, the in-silico representation must be for a specific real-world entity. Second, the twin should be updated over time. Third, the twin should be used to inform a decision. In computational cardiology digital twins offer the ability to improve patient care by both optimising therapy for a specific patient’s heart and by making predictions so therapy can be delivered in anticipation of a patient’s needs.
In this presentation I will describe how we are addressing this first and last components of the digital twin, by making computer models of individual patients at scale, within clinical timelines and using the twins to inform clinical therapies. This presentation will describe our work in making digital twins of heart failure patients and how we are using these twins to guide and evaluate cardiac resynchronisation therapy for these patients.