PI: Natalia Trayanova (Biomedical Engineering, WSE)

Co-I: David Spragg (Cardiology, SOM), Nikhil Paliwal (Alliance for Cardiovascular Diagnostic and Treatment Innovation)

To prevent recurrent ablation procedures in atrial fibrillation (AF) patients, we propose a data-driven technology that will enable a priori prediction of the success of pulmonary vein isolation (PVI). We will use existing AF patient clinical data and artificial intelligence to train predictive models for the success of PVI using catheter ablation. The overall goal of this technology is to provide clinical guidance as to which AF patients would benefit from PVI, thus maximizing the benefit of PVI while minimizing the financial costs and procedural risks of unnecessary ablation procedures.