Talk Summary:
Cardiac arrhythmias arise from disruptions in the heart’s electrical system and reflect complex interactions between anatomy, tissue properties, and electrophysiological dynamics. Despite advances in diagnosis and treatment, many arrhythmias remain difficult to predict, treat, and manage due to substantial interpatient variability and incomplete mechanistic understanding of how electrical activity is generated and sustained in the heart. Cardiac digital twins offer a framework for addressing these challenges by providing patient-specific computational representations of cardiac anatomy and electrophysiological behavior. When generated from routinely acquired clinical data such as cardiac magnetic resonance imaging and the standard twelve-lead electrocardiogram, these models enable systematic investigation of arrhythmia mechanisms and individualized responses to therapy. However, their development is limited by several fundamental challenges, including ensuring unique model solutions, constructing workflows compatible with clinical data and practice, and understanding how faithfully models represent true cardiac function. Recent work has focused on addressing these challenges through advances in model identifiability and approaches that support meaningful clinical translation.

Presenter Biography:
Dr. Karli Gillette is an Assistant Professor in the Department of Biomedical Engineering at the University of Utah and a faculty member of the Scientific Computing and Imaging (SCI) Institute. She earned her PhD in Mathematics from the University of Graz, Austria, and holds a master’s degree in Biomedical Engineering as well as bachelor’s degrees in applied mathematics and bioengineering from the University of Utah. Her research is centered on developing mathematical frameworks for cardiac digital twins, with the goal of advancing the understanding of arrhythmic heart disease. By moving beyond generic electrophysiological representations toward approaches that account for patient‑specific variability, her work seeks to strengthen the reliability and interpretability of cardiac digital twins and support their future use in precision cardiovascular medicine.

Author
Karli Gillette
University of Utah