Clinical Applications of Artificial Intelligence in Structural Heart Disease

Area of Science:

• Cardiovascular Medicine and Interventional Cardiology
• Medical Imaging and Artificial Intelligence

Background:

• Transcatheter cardiac interventions offer less invasive options for structural heart disease.
• Increasing procedural complexity necessitates advanced decision-support tools.
• Artificial intelligence (AI) is emerging as a key technology in cardiology.

Purpose of the Study:

• To review the current clinical applications of AI in structural heart disease.
• To highlight AI's role in diagnosis, disease progression assessment, risk stratification, procedural planning, and outcome prediction.

Main Methods:

• Comprehensive literature review of AI applications in structural heart disease.
• Analysis of studies demonstrating AI's capabilities in imaging analysis, hemodynamic assessment, and anatomical quantification.
• Focus on AI's comparative accuracy, efficiency, and reproducibility against conventional methods.

Main Results:

• AI demonstrates superior accuracy, efficiency, and reproducibility in cardiac imaging analysis and hemodynamic assessment.
• AI tools are increasingly utilized for diagnosis and assessing disease progression in structural heart disease.
• AI aids in risk stratification, procedural planning, and predicting clinical outcomes for patients.

Conclusions:

• AI is a valuable complementary tool in managing structural heart disease, supporting clinical decision-making.
• AI integration improves diagnostic accuracy, procedural planning, and prognostic assessment in interventional cardiology.
• Further research and clinical integration of AI are expected to advance structural heart disease care.