@inbook{17710,
  author = {Ambre Bertrand and Carolyna Yamamoto and Giulia Monopoli and Thomas Schrotter and Lena Myklebust and Julie Uv and Hermenegild Arevalo and Molly Maleckar},
  title = {Augmentation of Cardiac Ischemic Geometry for Improving Machine Learning Performance in Arrhythmic Risk Stratification},
  abstract = {Ventricular arrhythmias frequently occur as a complication of myocardial infarction (MI), due to significant changes in the heart{\textquoteright}s structure and electrophysiology. If left untreated, these alterations may lead to sudden cardiac death (SCD). It is therefore critical to evaluate risk prediction accurately in post-infarction patients to enable early intervention and improve patient outcomes. This work introduces a novel approach to improve arrhythmia risk assessment in post-infarction patients. We propose a new pipeline to build physiologically realistic image-based models of patient hearts, producing more realistic meshes compared to publicly available pipelines. We generate a library of 90 cardiac geometries of MI patients and use these cardiac models to estimate likelihood of reentry using electrophysiological (EP) simulations. However, due to the computationally expensive nature of this approach, we also introduce a data {\textellipsis}},
  year = {2024},
  journal = {Computational Physiology: Simula Summer School 2023 - Student Reports},
  pages = {39-53},
  month = {03/2024},
  publisher = {Springer},
  address = {Cham},
}