Artificial intelligence in coronary computed tomography angiography

Table 2 summary of the current literature on the prognostic value of machine learning algorithms in coronary computed tomography angiography

Ref.	Journal	Prospective	Multi Center	No. of Patients	No. of Events	Algorithm	Endpoint	Follow-up time	Performance
Motwani et al[48], 2017	European Heart Journal	Yes	Yes	10030	745 died	LogitBoost	5-yr all-cause mortality	5.4 ± 1.4 yr	AUC = 0.79
van Rosendael et al[47], 2018	Journal of Cardiovascular Computed Tomograph	Yes	Yes	8844	350 death and 259 non-fatal MI	XGBoost	MI and death	4.6 ± 1.5 yr	AUC = 0.77
Johnson et al[49], 2019	Radiology	No	No	6892	380 died of all causes and 70 died of CAD	Logistic regression, KNN, Bagged trees, and classification neural network	Death or cardiovascular events	9.0 yr (interquartile range, 8.2–9.8 yr)	For all-cause mortality: AUC = 0.77; For CAD deaths: AUC = 0.85
van Assen et al[50], 2019	European Journal of Radiology	No	No	45	16 MACEs	Regression analysis	MACE	12 mo	AUC = 0.94
von Knebel Doeberitz et al[51], 2019	The American Journal of Cardiology	No	No	82	18 MACEs	Integration of CT-FFR, stenosis ≥ 50% and plaque markers	MACE	18.5 mo (interquartile range 11.5 to 26.6 mo)	AUC = 0.94
Commandeur et al[52], 2020	Cardiovascular Research	Yes		1912	76 MI and/or cardiac death	ML	Long-term risk of MI and cardiac death	14.5 ± 2 yr	AUC = 0.82
Kwan et al[53], 2021	European Radiology	Yes	Yes	352		ML	Future revascularization		AUC = 0.78

XGBoost: Extreme gradient boosting; KNN: K-nearest neighbors; ML: Machine learning; AUC: Area under the curve; MACE: Major adverse cardiac events; CT-FFR: Computed tomography-fractional flow reserve; CAD: Coronary artery disease; MI: Myocardial infarction.