Synthetic fibrosis distributions for data augmentation in predicting atrial fibrillation ablation outcomes: an in silico study.

Introduction: Cardiac fibrosis influences atrial fibrillation (AF) progression and ablation outcomes, with late gadolinium enhancement (LGE) MRI providing a non-invasive tool to measure fibrosis distributions. While deep learning (DL) has shown promise in predicting ablation success, training such pipelines is limited by the availability of real patient data.

Methods: In this study, we generated synthetic fibrosis distributions using a denoising diffusion probabilistic model trained on a collection of 100 real LGE-MRI distributions. We incorporated them into 1,000 bi-atrial meshes derived from a statistical shape model and simulated AF episodes on them before and after various ablation strategies to expand the training dataset for DL-based outcome prediction. Our approach aims to improve the predictive performance of the DL pipeline by enhancing dataset diversity and better-capturing patient variability.

Results: We showed that the fibrosis distributions generated by the diffusion model closely resemble real LGE-MRI distributions, based on metrics such as mean intensities ( $1.1\pm 0.2$ vs. $1.1\pm 0.3$ ) and average Shannon entropy ( $0.77\pm 0.06$ and $0.81\pm 0.03$ ). AF biophysical simulations can be effectively conducted on bi-atrial meshes incorporating these synthetic distributions. Training the deep learning pipeline on these simulations produces performance metrics comparable to those achieved with real LGE-MRI distributions (ROC-AUC $=$ 0.952 vs. 0.943).

Conclusion: We have shown the ability of synthetic fibrosis distributions to be a data augmentation tool for deep learning classification of outcomes of various ablation strategies, which may enable rapid and precise assessment of atrial fibrillation treatment strategies.