Statistical shape modeling in cardiovascular disease: a narrative review.

Area of Science:

• Cardiovascular research
• Medical imaging analysis
• Biomedical engineering

Background:

• Cardiovascular diseases (CVDs) are a major global cause of death.
• Traditional methods may miss subtle anatomical variations crucial for CVD assessment.
• Statistical Shape Modeling (SSM) provides a quantitative approach to analyze anatomical structures.

Purpose of the Study:

• To explore the application of Statistical Shape Modeling (SSM) in cardiac anatomy assessment for cardiovascular diseases (CVDs).
• To review the evolution, methods, and clinical utility of SSM in diagnosing and managing CVDs.
• To highlight future directions for SSM in personalized cardiovascular medicine.

Main Methods:

• Utilizing advanced mathematical and statistical techniques to analyze geometric properties of cardiac structures.
• Employing landmark-based methods and point distribution models for shape analysis.
• Applying statistical techniques like principal component analysis for dimensionality reduction and variability measurement.

Main Results:

• SSM effectively captures and quantifies subtle variations in cardiac geometry.
• Key evaluation metrics like compactness, generalization, and specificity assess model performance.
• SSM demonstrates utility across CVD diagnosis, risk stratification, treatment optimization, and research.

Conclusions:

• SSM is a powerful tool for advancing cardiovascular disease diagnosis and treatment.
• Its ability to analyze subtle shape variations supports personalized medicine approaches.
• Future integration with deep learning and spatio-temporal analysis promises enhanced cardiac geometry assessment.