Artificial Intelligence in Ventricular Arrhythmias and Sudden Death

Area of Science:

• Clinical electrophysiology
• Medical artificial intelligence
• Cardiovascular disease mortality

Background:

• Sudden cardiac arrest (SCA) due to lethal ventricular arrhythmias is a significant global health concern, surpassing cancer in years of life lost.
• Current risk stratification methods are inadequate, leading to unexpected SCA events in individuals not identified as high-risk.
• Lethal ventricular arrhythmias pose a substantial clinical challenge in electrophysiology.

Purpose of the Study:

• To review the current literature on artificial intelligence (AI) applications in predicting lethal ventricular arrhythmias.
• To explore the potential of AI-based prediction models to enhance risk stratification for sudden cardiac arrest.
• To discuss future directions for AI in clinical electrophysiology for managing arrhythmias.

Main Methods:

• Systematic review of published literature on AI and ventricular arrhythmias.
• Synthesis of findings from studies utilizing AI for risk prediction in sudden cardiac arrest.
• Analysis of AI model performance in identifying individuals at high risk for lethal arrhythmias.

Main Results:

• AI tools are increasingly utilized to address complex challenges in clinical electrophysiology.
• AI-based prediction models, leveraging large datasets, show potential for improved risk stratification of lethal ventricular arrhythmias.
• The inadequacy of current tools highlights the need for advanced predictive methods.

Conclusions:

• Artificial intelligence holds significant promise for enhancing the prediction of lethal ventricular arrhythmias.
• AI can improve the identification of high-risk individuals, thereby reducing unexpected sudden cardiac arrest events.
• Further research and development in AI are crucial for advancing clinical electrophysiology and patient outcomes.