Artificial intelligence prediction models hold promise for personalized sudden cardiac death risk stratification, though important limitations and challenges currently hinder their widespread adoption.
AI and machine learning offer potential improvements over traditional LVEF-based risk stratification for sudden cardiac death, though significant challenges remain for clinical implementation.
Sudden cardiac death (SCD) remains a pressing health issue, affecting hundreds of thousands each year globally. The heterogeneity among people who suffer a SCD, ranging from individuals with severe heart failure to seemingly healthy individuals, poses a significant challenge for effective risk assessment. Conventional risk stratification, which primarily relies on left ventricular ejection fraction, has resulted in only modest efficacy of implantable cardioverter-defibrillators for SCD prevention. In response, artificial intelligence (AI) holds promise for personalized SCD risk prediction and tailoring preventive strategies to the unique profiles of individual patients. Machine and deep learning algorithms have the capability to learn intricate nonlinear patterns between complex data and defined end points, and leverage these to identify subtle indicators and predictors of SCD that may not be apparent through traditional statistical analysis. However, despite the potential of AI to improve SCD risk stratification, there are important limitations that need to be addressed. We aim to provide an overview of the current state-of-the-art of AI prediction models for SCD, highlight the opportunities for these models in clinical practice, and identify the key challenges hindering widespread adoption.
Kolk et al. (Fri,) conducted a review in Sudden cardiac death (SCD). Artificial intelligence (AI) prediction models vs. Conventional risk stratification was evaluated. Artificial intelligence prediction models hold promise for personalized sudden cardiac death risk stratification, though important limitations and challenges currently hinder their widespread adoption.