Machine learning-based predictive model for atrial arrhythmia following transcatheter atrial septal defect closure
We developed a novel ML-based risk model using a transfer learning approach to predict atrial arrhythmias after transcatheter ASD closure. Further research and external validation are needed to refine the proposed risk stratification prior to clinical implementation.
Authors
- Bert Vandenberk (ORCID: https://orcid.org/0000-0001-8296-920X)
- Pieter De Meester (ORCID: https://orcid.org/0000-0002-3865-0506)
- Alexander Van De Bruaene (ORCID: https://orcid.org/0000-0002-0469-8640)
- Joris Ector (ORCID: https://orcid.org/0000-0002-1460-7837)
- Werner Budts (ORCID: https://orcid.org/0000-0001-5850-5413)
- Peter Haemers (ORCID: https://orcid.org/0000-0002-3024-1281)
- Xander Jacquemyn (ORCID: https://orcid.org/0000-0001-8421-0686)
- Cedric Manlhiot (ORCID: https://orcid.org/0000-0001-9419-7681)
Institutions
- University of Toronto (CA)
- Hospital for Sick Children (CA)
- SickKids Foundation (CA)
- KU Leuven (BE)
Publication Details
- Journal
- International Journal of Cardiology Congenital Heart Disease
- Published
- 2025-11-06
- DOI
- https://doi.org/10.1016/j.ijcchd.2025.100639
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00