Digital Heart Twins Show Promise for AF Ablation Precision

Key Summary

• • Researchers developed patient-specific digital heart twins to improve planning for atrial fibrillation (AF) ablation.
  • The study compared three data sources: MRI heart scans, electrical voltage mapping, and conduction velocity measurements.
  • Electrical mapping data identified more potential AF targets than MRI alone.
  • Findings suggest that combining imaging and electrical data in a hybrid model may improve treatment precision.
  • The technology remains in the research stage, but could support more personalized AF care in the future.
  • For More Updates in Cardiology, Register for the ISCC2026

Digital Heart Twins May Improve Atrial Fibrillation Treatment Planning

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, affects millions worldwide and remains a leading contributor to stroke, heart failure, and hospitalization. While catheter ablation has become a cornerstone therapy for persistent AF, treatment outcomes vary significantly among patients. New research suggests that personalized digital heart twins may help clinicians identify optimal treatment targets and improve procedural planning.

How Can Digital Heart Twins Improve Atrial Fibrillation Ablation?

Researchers from Queen Mary University of London and collaborating institutions have developed patient-specific digital models designed to simulate the electrical activity of individual hearts affected by atrial fibrillation. Published in the Journal of Physiology, the study highlights how the quality and type of clinical data used to create these virtual models can significantly influence their accuracy.

Ablation procedures aim to eliminate abnormal electrical pathways that sustain AF by applying heat or cold energy to targeted cardiac tissue. However, persistent atrial fibrillation often involves widespread electrical remodeling that can be difficult to identify during a single procedure. As a result, repeat ablations are frequently required.

To address this challenge, investigators created detailed three-dimensional digital heart twins for nine patients. Each model was calibrated using three different clinical data sources:

• MRI scans identify cardiac scar tissue
• Electrical voltage mapping data
• Conduction velocity measurements that assess electrical signal propagation through heart tissue

The objective was to determine which data source most accurately identified the electrical circuits responsible for sustaining atrial fibrillation.

What Did the Study Reveal About Personalized Cardiac Models?

The findings demonstrated that electrical mapping data consistently identified more and often different ablation targets compared with MRI-based models alone. This suggests that structural imaging may not fully capture the complex electrical abnormalities underlying persistent AF.

Researchers found that voltage mapping and conduction velocity measurements provided complementary insights into the arrhythmia substrate. By integrating these datasets, clinicians may gain a more comprehensive understanding of patient-specific disease mechanisms.

Lead investigator Dr. Mahmoud Ehnesh noted that each data source captures a distinct aspect of atrial fibrillation behavior. Consequently, relying on a single modality could leave critical information undiscovered during treatment planning.

Why Hybrid Digital Heart Models Matter for Future AF Care

The study supports the development of hybrid digital heart twins that combine MRI imaging, voltage mapping, and conduction velocity data within a unified computational framework. Such models could help electrophysiologists predict treatment outcomes, identify optimal ablation sites, and potentially reduce repeat procedures.

For More Updates in Cardiology, Register for the ISCC2026

Although digital heart twin technology is not yet part of routine clinical practice, the research establishes an important scientific foundation for personalized AF management. For cardiologists, electrophysiologists, nurses, and cardiovascular care teams, these findings highlight the growing role of computational medicine and precision cardiology in shaping future arrhythmia treatment strategies.

As researchers continue refining these models, digital heart twins may become a valuable clinical decision-support tool, helping healthcare professionals deliver more targeted and effective care for patients with persistent atrial fibrillation.

Source:

Queen Mary University of London