Synthetic fibrosis distributions for data augmentation in predicting atrial fibrillation ablation outcomes: an in silico study
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.
Authors
- Yousef Mohammad (ORCID: https://orcid.org/0000-0003-1275-9391)
- Greg Slabaugh (ORCID: https://orcid.org/0000-0003-4060-5226)
- Omnia Alwazzan (ORCID: https://orcid.org/0000-0001-7416-1622)
- Keith Johnson (ORCID: https://orcid.org/0000-0002-0620-4560)
- Caroline H. Roney (ORCID: https://orcid.org/0000-0001-6809-0928)
- Alexander Zolotarev (ORCID: https://orcid.org/0000-0002-6049-5645)
Institutions
- Queen Mary University of London (GB)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-04-11
- DOI
- https://doi.org/10.3389/fcvm.2025.1512356
- Citations
- 2
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.78