Table 2.

Examples of application of machine learning techniques to echocardiographic research.

Study	Algorithm model	Brief algorithm description	Data source	Brief study description
Narula et al. (58)	(a) Support vector machine	Finds a gap in multidimensional data and classifies data based on gap	Echocardiographic data	To differentiate between athlete heart and hypertrophic cardiomyopathy
	(b) Random forest	Decision tree-based method derived from creating a number of decision trees
	(c) Artificial neural network	Learns in a manner similar to a biological network
Sengupta et al. (57)	Associative memory classifier-supervised learning	Used for making predictions based on a set of matrices. It is developed by observing co-occurrences of predictors from outcomes	Speckle tracking echocardiographic data	To differentiate between constrictive pericarditis and restrictive cardiomyopathy
Berikol et al. (48)	Artificial neural network		Echocardiographic data	Echocardiographic data and clinical factors used to stratify cardiovascular risk
Lancaster et al. (59)	Hierarchical clustering	It classifies similar objects into the same groups called clusters by building a hierarchy based on the distance between patients	Echocardiographic data	To investigate the natural clustering of echocardiographic variables to measure left ventricular dysfunction and isolate high-risk phenotyping patterns
Abdolmanafi et al. (38)	Deep learning	It creates layered neural networks to extract and transform features and learn in supervised and/or unsupervised manners	Coronary optical coherence tomography images	To automatically classify coronary artery layers in coronary optical coherence tomography images in Kawasaki disease
Bai et al. (60)			Cardiac magnetic resonance	Deep learning was used to analyze short and long axis cardiac magnetic resonance imaging and compare with human performance