AI in the EP lab - mapping, imaging, and signal interpretation

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Artificial intelligence (AI) leverages computational models to interpret complex cardiac data, including electrical signals and imaging.
• AI applications in electrophysiology (EP) aim to enhance arrhythmia diagnosis, treatment, and procedural decision-making.
• Personalized computational models create cardiac digital twins for procedural planning and hypothesis testing.

Purpose of the Study:

• To review current evidence on AI applications in the electrophysiology (EP) lab.
• To highlight AI's role in preprocedural planning, intraprocedural imaging, and the use of cardiac digital twins.
• To discuss practical workflows, clinical use cases, and limitations of AI in EP.

Main Methods:

• Synthesis of current evidence on AI in the EP lab.
• Review of AI integration with cardiac CT and MRI for automated segmentation and characterization.
• Analysis of AI-guided approaches for atrial fibrillation and ventricular tachycardia.

Main Results:

• AI integration with cardiac imaging automates segmentation and scar characterization, aiding in identifying ventricular tachycardia substrates.
• AI-guided approaches for atrial fibrillation show improved procedural consistency and clinical outcomes.
• Cardiac digital twins enable personalized procedural planning and hypothesis testing.

Conclusions:

• AI significantly enhances arrhythmia diagnosis and treatment by providing personalized insights and automating complex tasks.
• AI integration in the EP lab promises to reduce variability, improve substrate identification, and streamline workflows.
• Further development and validation are needed to overcome key limitations and fully realize AI's potential in cardiac electrophysiology.