Mahmoud Ehnesh remembers the moment the digital heart on his screen began to fibrillate in perfect sync with the patient in Room 4B at Royal Brompton Hospital—scars, electrical misfires, and all. It wasn’t magic, but the next best thing: a 'digital twin' of a human heart, built from real clinical data, now pulsing with life inside a computer. At Queen Mary University of London, Ehnesh and a team of researchers are refining these virtual replicas to transform how doctors treat atrial fibrillation (AF), a condition that disrupts the heart’s rhythm and affects more than 1.5 million people across the UK. For patients with persistent AF, ablation—a procedure that destroys rogue heart tissue—is often the best shot at restoring normal rhythm. But success is far from guaranteed, with repeat procedures common due to the complex, shifting electrical patterns that standard imaging can’t fully capture.
That’s where the digital twin comes in. By building personalized 3D models of patients’ hearts, researchers can simulate AF before surgery, pinpoint the exact circuits driving the chaos, and predict which ablation strategy will work best. But not all models are created equal. In a study published in The Journal of Physiology, the team tested how different types of clinical data shape the accuracy of these simulations. They built digital hearts for nine patients, calibrating each model using three distinct data sources: MRI scans that reveal scar tissue, voltage readings from electrical mapping, and measurements of conduction velocity—the speed at which electrical signals travel through heart muscle.
The results were striking. Models using only MRI data identified fewer arrhythmia triggers—and different ones—than those incorporating electrical measurements. Voltage and conduction data consistently revealed hidden pathways that imaging alone missed. This means a model based solely on MRI might send a surgeon after the wrong target. The study makes a powerful case for combining all three data types into a hybrid model, creating a fuller, more accurate picture of the heart’s electrical landscape. "Relying on any single source means missing part of the picture," says Dr. Ehnesh. "Combining all three within a single personalized model is the most promising path toward more accurate, targeted ablation for persistent AF patients."
The implications are profound. If digital twins can reliably predict ablation outcomes, they could reduce repeat procedures, shorten recovery times, and lower stroke risk for thousands. While the technology isn’t yet in routine clinical use, this research lays the scientific groundwork for that future. With collaborators from Imperial, King’s College, Leeds, and even IHU Liryc in Bordeaux, the London-led team is pushing the frontier of computational medicine—one heartbeat at a time. As digital models grow more sophisticated, the dream of precision cardiology inches closer to reality.