Johns Hopkins University scientists have developed a new tool for predicting which patients suffering from a complex inflammatory heart disease are at risk of sudden cardiac arrest. Published in Science Advances, their method is the first to combine models of patients’ hearts built from multiple images with the power of machine learning.

Lead author Julie K. Shade is a PhD candidate in the Department of Biomedical Engineering, conducting her research in the lab of Natalia Trayanova, co-director of the Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), and ICM core faculty member. “This robust new personalized technology outperformed clinical metrics in forecasting future arrhythmia and could transform the management of cardiac sarcoidosis patients,” said Trayanova. 

Read more here about the research behind this important article.