Table 1. Overview of ML algorithms applied to imaging.

Authors	ML algorithm	Aim	Performance
Seah, et al.[22]	Neural network	To visualize chest radiograph features of congestive heart failure	AUC: 0.82
Playford, et al.[23]	Multidimensional clusters	To infer aortic valve area from other echocardiographic data, without the need for any left ventricular outflow tract measurements	AUC: 0.95
			AUPRC: 0.73
Narula, et al.[24]	Support vector machine	Automated discrimination of hypertrophic cardiomyopathy from physiological hypertrophy of the athletes	Overall sensitivity: 87%
	Random forest		Overall specificity: 82%
	Artificial neural network
Madani, et al.[25]	Neural network	View classification of echocardiograms	Accuracy: 97.8%
Otha, et al.[26]	Convolutional neural network	To detect and classify myocardial delayed enhancement pattern	Accuracy: 87.2%–88.9%
González, et al.[27]	Convolutional neural network	To calculate Agatston score from non-enhanced chest CT without prior segmentation of coronary artery calcification	Pearson correlation coefficient between the reference standard and the computed scores on the test set: 0.932
Tao, et al.[28]	Convolutional neural network	Fully automated quantification of LV from cine MR and to evaluate its performance in a multivendor and multicenter setting	The average perpendicular distance compared with manual analysis was 1.1 ± 0.3 mm
Ngo, et al.[29]	Deep neural network	Automated segmentation of LV from cine magnetic resonance imaging	It outperformed manual segmentation

AUC: area under the curve; AUPRC: area under the precision recall curve; LV: left ventricle; ML: machine learning.