Forty per cent of sudden cardiac death (SCD), the leading cause of mortality worldwide, occurs when the electrical system of the heart malfunctions, generating an often-fatal rhythm known as ventricular tachycardia (VT). While anti-arrhythmic drugs and defibrillators have been the primary therapeutic strategies for preventing SCD due to VT, radiofrequency catheter ablation (CA), which destroys the ability of cardiac tissue to conduct electrical signals, offering the possibility of permanent cure, has emerged as a potent therapeutic alternative. However, the success of CA has been limited by inaccuracies in identifying critical site responsible for generating VT, which is not surprising considering the interplay of factors that need to be considered simultaneously.
This project will examine the use a novel Deep Learning approach that incorporates raw data from traditional mapping during VT ablation and harnesses the power of AI to predict critical VT sites and guide VT ablation, that will ultimately lead to improvements in VT ablation outcomes.
Last updated17 January 2023