Sudden Cardiac Death (SCD) is a pressing global health issue, responsible for up to 20% of all deaths in Western societies. Current guidelines for SCD prevention, which predominantly rely on ejection fraction (EF) measurements, exhibit considerable limitations in their ability to accurately assess risk across diverse populations. This often leads to both underdiagnosis and overtreatment. Consequently, there is a growing interest in refining these guidelines by incorporating personalized prediction models to more effectively identify individuals at risk of SCD.
The primary objective of this project is to develop innovative artificial intelligence (AI)-based solutions that integrate multiple imaging modalities to improve early prediction of Sudden Cardiac Death. With this effort, we also aim to translate key SCD predictors identified through advanced imaging techniques into more accessible modalities, such as ECG, enabling earlier identification of at-risk individuals.
This interdisciplinary project is a collaborative effort between our Cardiovascular laboratory and the Department of Cardiology at the Amsterdam Medical Centre. Together, we strive to advance the field of SCD prevention and improve patient outcomes through the development and implementation of cutting-edge AI-driven approaches.