Quantification of Myocardial Contraction Fraction with Three-Dimensional Automated, Machine-Learning-Based Left-Heart-Chamber Metrics: Diagnostic Utility in Hypertrophic Phenotypes and Normal Ejection Fraction.

The differentiation of left ventricular (LV) hypertrophic phenotypes is challenging in patients with normal ejection fraction (EF). The myocardial contraction fraction (MCF) is a simple dimensionless index useful for specifically identifying cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) when calculated by cardiac magnetic resonance. The purpose of this study was to evaluate the value of MCF measured by three-dimensional automated, machine-learning-based LV chamber metrics (dynamic heart model [DHM]) for the discrimination of different forms of hypertrophic phenotypes.

Three-Dimensional Automated, Machine-Learning-Based Left Heart Chamber Metrics: Associations with Prevalent Vascular Risk Factors and Cardiovascular Diseases.

Background. Three-dimensional transthoracic echocardiography (3DE) powered by artificial intelligence provides accurate left chamber quantification in good accordance with cardiac magnetic resonance and has the potential to revolutionize our clinical practice. Aims.

The complex interplay among atherosclerosis, inflammation, and degeneration in ascending thoracic aortic aneurysms.

Objective: To assess the histopathological findings of a large series of ascending thoracic aortic aneurysm (TAA) surgical specimens applying the updated classification on noninflammatory degenerative and inflammatory aortic diseases proposed by the Association for European Cardiovascular Pathology and the Society for Cardiovascular Pathology clinicopathological correlations.

Methods: A total of 255 patients surgically treated for ascending TAA were enrolled. Surgical ascending aorta specimens were examined.