Artificial intelligence and machine learning in cardiovascular computed tomography

Table 2 Machine learning studies in computed tomography

Ref.	ML approach	Brief study description
ML derived CAC assessment
Al’Aref et al[24]	Multiple ML algorithm	To use CAC and clinical factors for CAD prediction
Tesche et al[26]	ML algorithm	To compare ML derived CT FFR and CAC in CT
Kay et al[27]	ML algorithm	To identify phenotypes of left ventricular hypertrophy in combination with CAC
ML derived CT FFR assessment
Zhou et al[31]	Multiple ML algorithms	To employ CT FFR for myocardial bridge formation prediction
Tang et al[32]	ML algorithm	To compare ML CT FFR, CTA and invasive angiography
Coenen et al[33]	Supervised learning	To identify CAD
ML derived evaluation of plaque characteristics
Dey et al[34]	ML algorithm	To generate ML derived scores from plaque characteristics
Hell et al[35]	ML algorithm	To predict cardiac death from plaque characteristics from CTA
ML derived evaluation of epicardial adipose tissue
Rodrigues et al[38]	ML algorithm	To segment and distinguish between different varieties of EAT
Commandeur et al[39]	Deep learning	To quantify EAT in CT
Otaki et al[40]	Supervised learning	To assess the relationship between EAT in CT and MFR in PET
Miscellaneous applications of ML in CT
Baskaran et al[41]	Deep learning	To assess automatic and manual assessment of left and right cardiac structure and function
Al’Aref et al[42]	Supervised learning	To identify culprit coronary lesions in CT
Beecy et al[43]	Deep learning	To detect acute ischemic stroke in CT
Oikonomou et al[44]	Supervised learning	To utilize perivascular fat for cardiac risk prediction
Eisenberg et al[45]	Deep learning	To evaluate epicardial tissue for MACE events

CT: Computed tomography; CTA: Computed tomography angiography; CAC: Coronary artery calcium; ML: Machine learning; CAD: Coronary artery disease; FFR: Fractional flow reserve; EAT: Epicardial adipose tissue.