Table 2.

Overview of Machine Learning Algorithms Applied in Cardiovascular Imaging Studies

Author (Year)	N	Study Design	Methods	Measures	Main Findings
Berchialla (2012)7	228	Cross-sectional	Bayesian network Logistic regression Random forest Artificial neural network SVM	Use data from stress echo and CTA to predict future cardiovascular events (myocardial infarction or death)	Bayesian network outperformed other methods Measures of LV dysfunction and CAD extent had greater impact in predicting target event
Isgum (2012)8	584	Longitudinal	Linear and quadratic discriminant k-NN SVM	Automatically score coronary calcium in low-dose, non-contrast-enhanced chest CT scans	Cardiovascular risk was best determined by merging results of 3 best-performing classifiers (2-stage classification with k-NN, 2-stage classification with k-NN and SVM, 1-stage classification with k-NN with selected features) Detected on average 157/198 mm3 (sensitivity 79.2%) of coronary calcium volume with average 4 mm3 false positive volume
Lee (2013)9	205	Cross-sectional	Decision tree Naive Bayes k-NN SVM	Analyze AAA geometry on contrast CT images Determine whether AAA wall surface curvatures predict rupture risk	k-NN demonstrated the highest accuracy (85.5% compared to 68.9% using maximum diameter alone) Accuracy of SVM, decision tree, and naive Bayes was 83.4%, 83.3%, and 80.1%, respectively
Mohammadpour (2015)10	115	Cross-sectional	Fuzzy rule-based classifying system	Use myocardial perfusion scan and clinical variables to predict CAD	Classifier determined most important risk factors for CAD and correctly detected patients who did not need invasive coronary angiography with 92.8% accuracy
Xiong (2015)11	140	Cross-sectional	Naive Bayes Random forest AdaBoost	Determine physiologic manifestation of coronary stenoses by assessing myocardial perfusion on CTA images	Method may improve diagnosis of obstructive coronary artery stenoses AdaBoost performed better than other algorithms with accuracy 0.70, sensitivity 0.79, and specificity 0.64
Knackstedt (2015)12	255	Cross-sectional	Vendor-independent software AutoLV	Obtain measures of LV volumes, EF, and average biplane longitudinal strain using ultrasound images Compare values with visual estimation and manual tracking	Algorithm was time efficient (8±1 sec/patient), reproducible, and technically feasible for LVEF and longitudinal strain assessment
Arsanjani (2015)13	713	Longitudinal	Machine-learning algorithm LogitBoost	Use SPECT perfusion data to predict early revascularization in patients with suspected CAD	LogitBoost sensitivity (73.6±4.3%) for predicting revascularization was similar to one expert reader (73.9±4.6%) and perfusion measures only (75.5±4.5%) LogitBoost specificity (74.7±4.2%) was better than both expert readers (67.2±4.9% and 66.0±5.0%) and similar to total ischemic perfusion deficit (68.3±4.9%) LogitBoost AUC (0.81±0.02) was identical to one reader but superior to another reader (0.72±0.02) and perfusion measures only (0.77±0.02)
Berikol (2016)14	228	Longitudinal	SVM Artificial neural network Naive Bayes Logistic regression	Diagnose acute coronary syndrome and decide whether to discharge or admit patients considering their symptoms, electro- and echocardiographic findings, levels of cardiac enzymes	SVM had the highest predicting accuracy 99.13%, sensitivity 98.22%, and specificity 100% Accuracy of artificial neural network, naive Bayes, and logistic regression was 91.26%, 88.75%, and 90.1%, respectively
Celutkiene (2016)15	256	Longitudinal	Custom multi-parametric mathematical model	Analyze dobutamine stress echocardiography with speckle tracking (compared to conventional wall motion analysis) to detect myocardial ischemia	Algorithm detected myocardial ischemia in patients with coronary stenoses ≥50% with sensitivity 91.6% and specificity 86.3%, compared to 76.8% and 89%, respectively, for visual assessment
Motwani (2016)16	10,030	Longitudinal	Custom-built predictive classifier	Predict 5-year all-cause mortality in patients with suspected CAD undergoing CCTA	Method showed performance superior to use of clinical and CCTA findings alone AUC was 0.79 vs. 0.61 for Framingham risk score, 0.64 for segment stenosis score, 0.64 for segment involvement score, and 0.62 for modified Duke index

AAA - abdominal aortic aneurism, AUC - area under the curve, CAD - coronary artery disease, CCTA - coronary CTA, CT - computed tomography, CTA - CT angiography, EF - ejection fraction, k-NN - k-nearest neighbor, LV - left ventricle, SVM - support vector machine