Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibrillation
By systematically optimizing the EGF Model's hyperparameters based on clinical outcomes, we reliably detect and target key AF sources that, when ablated, improve procedural success. These findings, supported by the FLOW-AF trial, underscore the usefulness of clinical outcome-based machine learning to improve the efficacy of algorithm based medical diagnostics.
Authors
- Peter Ruppersberg (ORCID: https://orcid.org/0000-0002-4612-1778)
- Melissa H. Kong (ORCID: https://orcid.org/0000-0001-7140-5935)
- Andreas Rillig (ORCID: https://orcid.org/0000-0003-3457-0936)
- Kostiantyn Ahapov
- Tamás Szili‐Törok (ORCID: https://orcid.org/0000-0002-7165-8243)
- Philip Haeusser
- Georg Nölker (ORCID: https://orcid.org/0000-0003-2789-8247)
- Steven Castellano (ORCID: https://orcid.org/0000-0003-1772-4702)
- Stefan G. Spitzer (ORCID: https://orcid.org/0000-0001-7822-2154)
Institutions
- Universität Hamburg (DE)
- University of Szeged (HU)
- Heart and Diabetes Center North Rhine-Westphalia (DE)
- Society of Interventional Radiology (US)
- University Medical Center Hamburg-Eppendorf (DE)
- Corium (United States) (US)
- PharmatrophiX (United States) (US)
- Cortex Pharmaceuticals (United States) (US)
- Klinik und Poliklinik für Psychotherapie und Psychosomatik (DE)
- Brandenburg University of Technology Cottbus-Senftenberg (DE)
- Technische Universität Dresden (DE)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-08-26
- DOI
- https://doi.org/10.3389/fcvm.2025.1517484
- Citations
- 2
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.78