Artificial intelligence and extended reality in TAVR: current applications and challenges

Area of Science:

• Cardiovascular Medicine
• Medical Technology
• Artificial Intelligence

Background:

• Severe aortic stenosis management involves complex diagnostic and planning procedures.
• Transcatheter aortic valve replacement (TAVR) is a key treatment option requiring precise patient assessment.
• Current methods may have limitations in accuracy and visualization.

Purpose of the Study:

• To evaluate the integration of AI and XR in TAVR.
• To assess the impact of these technologies on diagnostic accuracy, risk stratification, and pre-procedural planning.
• To explore the potential of AI and XR in improving TAVR outcomes.

Main Methods:

• Utilized advanced AI algorithms for electrocardiographic, echocardiographic, and CT-based assessments.
• Employed immersive XR technologies (augmented, virtual, mixed reality) for spatial visualization and procedural guidance.
• Analyzed data to determine improvements in precision and patient-specific risk prediction.

Main Results:

• AI algorithms reduced observer variability and enhanced patient-specific risk prediction.
• XR technologies improved spatial visualization of cardiac anatomy and supported real-time guidance.
• Integration of AI and XR demonstrated significant potential in revolutionizing TAVR management.

Conclusions:

• AI and XR integration offers a transformative approach to managing severe aortic stenosis via TAVR.
• Enhanced diagnostic accuracy and procedural planning are key benefits.
• Standardized protocols, regulatory frameworks, and ethical considerations are crucial for clinical adoption.