TABLE 3.

Clustering in Cardiac Imaging

First Author (Ref. #)	Assessment	Type of Clustering	Result
Ernande et al. (27)	Left ventricular function	Agglomerative Hierarchical Clustering	3 clusters with significantly different echocardiographic phenotypes of type 2 diabetes mellitus.
Sanchez-Martinez et al. (28)	Heart failure with preserved ejection fraction	Agglomerative Hierarchical Clustering	2 groups to capture healthy and heart failure patients with preserved ejection fraction.
Shah et al. (29)	Heart failure with preserved ejection fraction	Agglomerative Hierarchical clustering and model-based clustering	Hierarchical clustering to conceptualize similar and redundant features of phenotypic features. Model-based clustering with Gaussian distribution identified 3 clusters where cluster 1 had the least severe electric and myocardial remodeling while cluster 3 had the most severe.
Katz et al. (30)	Heart failure with preserved ejection fraction	Model-based clustering	2 clusters with significantly different phenogroups of hypertensive patients.
Omar et al. (40)	Left ventricular diastolic dysfunction	Agglomerative Hierarchical Clustering	3 groups corresponding to severity of diastolic dysfunction based on speckle-tracking echocardiography data.
Lancaster et al. (32)	Left ventricular diastolic dysfunction	Agglomerative Hierarchical Clustering	2 groups with the severity of diastolic dysfunction.
Horiuchi et al. (33)	Acute heart failure	k-means clustering	3 clusters with vascular failure, renal failure, and older patients with atrial fibrillation and preserved ejection fraction, respectively.
Bansod et al. (34)	Endocardial border estimation and ellipse fitting on more than 10 video sequences	Density-based spatial clustering of applications with noise	Endocardial border estimation using density-based spatial clustering of applications with noise.
Carbotta et al. (35)	Indication of coronary heart disease	Oblique principal components clustering procedure	2 groups with elevated thyroid parameter with reduced cardiovascular parameter increased metabolic parameter to indicate coronary heart disease.
Peckova et al. (36)	Diastolic dysfunction	Hierarchical Clustering	2 groups showed no association of deterioration of left ventricular relaxation with a mild-to-moderate decrease in eGFR.