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. |