Copyright
©The Author(s) 2021.
World J Cardiol. Oct 26, 2021; 13(10): 546-555
Published online Oct 26, 2021. doi: 10.4330/wjc.v13.i10.546
Published online Oct 26, 2021. doi: 10.4330/wjc.v13.i10.546
Table 1 Type of machine learning
Types of machine learning | Function | Examples |
Supervised learning (55) | Contains labels and outcomes, deduces inferences for prediction purpose | Includes logistic regression, ridge regression, elastic net regression, Bayesian and artificial neural networks |
Unsupervised learning (55) | No labels, independently detects significant relationships. | Includes hierarchical clustering, k- means clustering, principal component analysis |
Semi-supervised learning (55) | Properties of both supervised and unsupervised learning | Utilized in image and speech recognition |
Re-enforcement learning (55) | Utilizes reward function to execute tasks | Utilized in medical imaging, analytics, and prescription selection |
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 |
Table 3 Big data utilization by machine learning in computed tomography
- Citation: Seetharam K, Bhat P, Orris M, Prabhu H, Shah J, Asti D, Chawla P, Mir T. Artificial intelligence and machine learning in cardiovascular computed tomography. World J Cardiol 2021; 13(10): 546-555
- URL: https://www.wjgnet.com/1949-8462/full/v13/i10/546.htm
- DOI: https://dx.doi.org/10.4330/wjc.v13.i10.546