Table 5. Application of Coronary Stenosis and Plaque.

Study	Year	Role of Artificial Intelligence	Dataset for Test	Performance	Algorithm
Coenen et al. [44]	2018	Prediction of FFR based on synthetic data (manual segmentation of coronary tree) in CCTA	351	0.78 accuracy	Machine learning using synthetic stenosis model and computational fluid dynamics results
Zreik et al. [47]	2019	Automatic plaque and stenosis characterization in stretched MPR image of CCTA	65	0.77 accuracy	3D recurrent CNN
van Hamersvelt et al. [46]	2019	Identification of patients with functionally significant coronary stenosis in CCTA	101	0.76 AUC	CNN for LV myocardial segmentation; SVM for patient classification
Wolterink et al. [48]	2019	Coronary centerline extraction in CCTA	24	93.7% overlap	3D CNN for prediction of vessel orientation and radius to guide iterative tracker
Wu et al. [19]	2019	Coronary artery tree segment labeling in CCTA	436	0.87 F1	Bidirectional LSTM in three graph representation
Hong et al. [27]	2019	Quantification of coronary stenosis automatically in CCTA	156	0.95 correlation coefficient	U-Net
Zreik et al. [45]	2020	Identification of a patient requiring invasive coronary angiography in stretched MPR image of CCTA	137	0.81 AUC	Autoencoder and SVM
Kumamaru et al. [21]	2020	Fully automatic estimation of minimum FFR from CCTA (i.e., free from human input)	131	0.76 accuracy	Lumen extraction block using GAN; residual extraction block; prediction block for minimum FFR estimation

AUC = area under the receiver operating characteristic curve, CCTA = coronary computed tomography angiography, CNN = convolutional neural network, CT = computed tomography, D = dimensional, ECG = electrocardiography, FFR = fractional flow reserve, GAN = generative adversarial network, LSTM = long short-term memory, LV = left ventricle, MPR = multiplanar reformatted, SVM = support vector machine