Artificial intelligence in coronary computed tomography angiography

Table 1 Summary of the current literature on machine learning-based fractional flow reserve-computed tomography

Ref.	Journal	Prospective	Multi- or single center	Platform	No. of patients	No. of vessels	Compared with CT-FFRCFD	Accuracy	AUC
Itu et al[23], 2016	Journal Application Physiology	No	Single center	-	87	125	Yes	Per-lesion: 83%	Per-lesion: 0.90
Coenen et al[25], 2018	Circulation: Cardiovascular Imaging	Yes	The MACHINE registry	cFFR, version 2.1, Siemens	351	525	Yes	Per-lesion: 78%Per-patient: 85%	Per-lesion: 0.84
Tesche et al[26], 2018	Radiology	No	Single Center	cFFR, version 1.4, Siemens	85	104	Yes	Per-lesion: 88%; Per-patient: 92%	Per-lesion: 0.89; Per-patient: 0.91
Mastrodicasa et al[34], 2019	Journal of Cardiovascular Computed Tomograph	No	Single center	cFFR, version 3.0, Siemens	10/40	160	No	IRIS: 82%; FBP: 82%	-
Baumann et al[32], 2019	European Journal of Radiology	No	The MACHINE registry	cFFR, version 2.1, Siemens	351	525	No	-	Per-patient: Women:0.83; Men: 0.83
Doeberitz et al[27], 2019	European Radiology	No	Single center	cFFR, version 2.1, Siemens	48	103	No	-	Per-lesion: 0.93
Wang et al[24], 2019	Journal of Geriatric Cardiology	Yes	Single center	DEEPVESSE-FFR Platform	63	71	No	Per-lesion: 89%; Per-patient: 87%	Per-lesion: 0.93; Per-patient: 0.93
Tesche et al[30], 2020	Journals of the American College of Cardiology: Cardiovascular Imaging	Yes	The MACHINE registry	cFFR, version 2.1, Siemens	314	482	No	Per-lesion: 78%; CAC ≥ 400: 76%CAC 0-100: 79%; CAC 100-400: 76%	Total: 0.84 CAC ≥ 400: 0.71; CAC 0-400: 0.85
De Geer et al[31], 2019	American Journal of Roentgenology	No	The MACHINE registry	cFFR, version 2.1, Siemens	351	525	No	Total: 78%; 80 kv: 86%; 100 kv: 77%; 120 kv: 78%	Total: 0.84; 80 kv: 0.90; 100 kv: 0.82; 120 kv: 0.84
Xu et al[33], 2020	European Radiology	No	10 individual centers across China	cFFR, version 3.2.0, Siemens	437	570	No	Total: 89%; High quality: 94%; Low quality: 83%	Total: 0.89; High quality: 0.93; Low quality: 0.80
Kumamaru et al[28], 2020	European Heart Journal - Cardiovascular Imaging	No	Multi-center	Python 3.6	131	-	No	Per-patient: 76%	Per-patient: 0.78
Li et al[29], 2021	Acta Radiologica	No	Single center	DEEPVESSE-FFR Platform	73	85	No	Per-lesion: 92%; Per-patient: 91%	Per-lesion: 0.96
Xu et al[35], 2020	European Radiology	No	A Chinese multicenter study	cFFR, version 3.1.0, Siemens	442	544	No	Per lesion: 90%	-

IRIS: Iterative reconstruction in image space; FBP: Filtered back projection; CAC: Coronary artery calcium; FFR: Fractional flow reserve; AUC: Area under the curve; CT: Computed tomography.

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.