To examine the technical pathways and clinical value of integrating digital twins and artificial intelligence in atrial fibrillation management.
Approach:
Key Findings:
A relatively complete technical chain for constructing patient-specific cardiac digital twins has been established, achieving anatomical Dice coefficients of 93% or higher and a correlation coefficient for activation time prediction exceeding 0.96.
AI-ECG increased AF detection rates by 2.3-fold.
Models predicting post-ablation recurrence achieved AUC values generally ranging from 0.72 to 0.85.
Intra-operative three-dimensional reconstruction time was reduced to 65 seconds.
The integration of the virtual closed-loop framework has been preliminarily validated in scenarios such as drug screening, ablation planning, and thrombus risk assessment.
Interpretation:
The integration of digital twins and AI offers a new pathway for comprehensive atrial fibrillation management.
Limitations:
Clinical application relies on further validation through multicentre studies and high-quality evidence.
Conclusion:
As technology matures and evidence accumulates, this model is expected to be gradually introduced into clinical practice.
