Introduction
An event series for Turing-Roche partnership updates, knowledge sharing and new perspectives. Find out more about the series.
This event will be on the theme of digital twins in health. A digital twin is a virtual replica of a physical process or system that is dynamically updated using data collected from real-time monitoring of its physical counterpart. There has been recent interest in how these can be developed to investigate disease states, predict patient response to treatment and ultimately improve health and wellbeing.
About the event
We will be hearing from Steven Niederer, Professor of Biomedical Engineering at Imperial College London and co-director of the TRIC-DT: a Turing Institute Research and Innovation Cluster (TRIC) focusing on digital twins (DT) and Jon Walsh, co-founder and the Head of Modeling at Unlearn.AI.
Steve's talk will introduce the TRIC-DT and other recent research: Digital Twins have a huge potential to impact healthcare. However, many early digital twin examples have focused on small numbers of cases due to model complexity and the costs of twin calibration and forecasts. In the health TRIC-DT we will invest in technologies to reduce barriers to building digital twins at scale and demonstrate the ability to build digital twins that track patients through time. We will build on our early work in multi-scale patient specific cardiac modelling that provides a framework to link emergent clinical observations with underpinning molecular mechanisms.
Jon's talk will present findings from a joint project with Roche colleagues; applying a machine learning model of Alzheimer's disease progression to the control arm of the ABBY study (NCT01343966). The model is capable of generating digital twins, which are comprehensive predicted control outcomes for trial participants. Regulatory review and guidance suggest that digital twins may be used to decrease sample size clinical trials, even in pivotal studies. We will discuss the results applying the technology to the ABBY study, which show that this approach can yield significant sample size savings in future Alzheimer's disease clinical trials.
Watch now
You can watch a recording of this event here.
