A digital twin of your own heart

How much could the technology of digital twins aid us in the diagnosis of cardiac disorders? Queen Mary University of London’s Caroline Roney…

Caroline - The average heart beats a hundred thousand times a day. But we don't really like to talk about averages because we work in personalised healthcare. And digital twins are all about personalising treatments to the patient. So although we'd like to have this regular heartbeat, what can actually happen is the top chambers of the heart, so the atrium, they can quiver rather than having a strongly coordinated contraction. And this irregular heart rhythm is called atrial fibrillation. And this is a bad thing for patients because it increases the risk of stroke and heart failure and it reduces the quality of life. And there are treatments for atrial fibrillation, including antiarrhythmic drugs and catheter ablation therapy. But there's actually a very big variation between patients, and this is why this is a good candidate for digital twins because all of our hearts are different. So we have different shapes, different sizes, the hearts. We have differences in our electrical properties and our scar tissue. And all of these properties together mean that which treatment is best and whether a treatment is likely to work will vary between patients.

Will - So how do you go about making a digital twin of the heart? Say, I came in and I was worried about my atrial fibrillation. What kind of data would you take and how would you input it into a meaningful system?

Caroline - So you might have some imaging done, so you might have an MRI or a CT scan. If you have that done, then that means that we can get a really detailed picture of the shape of the heart. We'd be able to make a personalised anatomy. That's a model that's personalised to your specific anatomy, but it's not just about the shape of the heart. We also need to be able to capture the electrical properties. So if we have your electrical signals or your ECG, we can use that data then to calibrate the digital twin and to calibrate the mathematical model. And then once we have this virtual beating heart, we can then try out different treatments. So we could burn different areas of the heart in different places to see whether that's likely to work, or we can add different antiarrhythmic drugs to the virtual twin, or we can do combinations of the two. Actually, like for anyone who's interested in cardiac digital twins, there's a new exhibit at the moment at the Science Museum where you can see a very cool beating heart. It's a patient specific model. You can see it beating, you can see the blood flow, and this will give you an idea for what these models look like.

Will - Now, as you said at the beginning of this, the heart is always beating a hundred thousand times a day. So are you always taking data? Is there something attached to the wearer that allows them to get a continuous stream of information about the heart?

Caroline - It depends on what we're modelling. So sometimes we might make a model that's based just on imaging data and a single ECG, and that captures how the patient is at that moment in time. But as you say, it's very important to include how we change over time, and particularly after having a procedure to get an idea of how the heart's changing. So in those cases, we're moving towards using wearables, so we're moving towards using Apple Watch or Fitbit and taking the signals from there. But that's kind of the next step of our modelling to include that longitudinal data and to really update the model over time.

Will - How does this improve diagnosis and treatment in a way that previous methods haven't?

Caroline - One of the ways that we're approaching our modelling is we really want to include data from large populations of patients and combine this with patient specific models so that you have the advantages of what you've learned across large populations of data, but together with patient specific models which capture lots of the physics and the physiology of the problem and their personalised to the patient. And we found that by combining these two approaches and adding the patient specific models to our predictions, we were able to improve our predictions of the long-term outcome of atrial fibrillation procedures by 30%.

Will - That's a remarkable difference.

Caroline - Yeah. So I think going forwards, it's really going to be, in my opinion, about combining the two approaches so that you get the advantages of the large data sets and what we know across populations and on what works. And we use machine learning to combine that with the detailed physics-based models.

Will - It sounds like then, given that this is so broadly applicable but also fantastically specialised to the individual, it could help vast amounts of people.

Caroline - So there's 1.5 million people in the UK at the moment with atrial fibrillation. So it is a large health problem, and digital twins in healthcare can be applied across all different organs and all different diseases. So I hope this will be a very exciting area and have real impact.

Will - Do you think then that the future could well be, I mean the heart is one system and a fairly complicated one at that, could you envisage full body versions of digital twins that incorporate all kinds of organs and systems?

Caroline - I think it's possible, but we do have a way to go to make these virtual human twins the whole body because it's very complicated to couple together all of the different systems, but we are actually already thinking about the logistics of this. We need to really think about how to store the data, share models and deploy in the clinic. So at Queen Mary, we're part of a large initiative that's led by the Virtual Physiological Human Institute that's about how to make an ecosystem for digital twins in healthcare. So this is really thinking about how we store our data? How can we share our models, deploy in the clinic about the ethics of it and really about sustainability as well. So as part of that initiative, we're focusing on some use cases like cardiovascular. There's a cancer use case or osteoporosis and intensive care, but the overall vision is to do this, to move towards a whole virtual human twin. But I think that'll be in the future.