Table 1.
Characteristics of studies included in the systematic review
| Authors | Research purpose | Patients, n | Scan mode | AI method | Performance |
|---|---|---|---|---|---|
| (Rodrigues et al. 2015) | Automated segmentation of epicardial and mediastinal fats | 20 | Non-contrast CT | ML: Intersubject registration + RF | DSC = 0.968 |
| (Rodrigues et al. 2016) | Automatic segmentation and quantification of cardiac fats | 20 | Non-contrast CT | ML: Atlas-based + RF | DSC = 0.977 |
| (Rodrigues et al. 2017b) | Automated segmentation of epicardial fat | 20 | Non-contrast CT | ML: Genetic algorithms | The percentage of epicardial fat engulfed by the ellipse was 99.5% |
| (Norlén et al. 2016) | Automatic segmentation and quantification | 30 | CCTA | ML: Multi-atlas + RF + Markov random field |
CC = 0.99 DSC = 0.91 |
| (Zlokolica et al. 2017) | Semiautomatic EAT segmentation | 10 | CCTA | ML: Fuzzy c-means clustering + geometric ellipse fitting | DSC = 0.69 |
| (Commandeur et al. 2018) | Segmentation and quantification of EAT | 250 | Non-contrast CT | DL: CNN |
CC = 0.924 DSC = 0.823 |
| (Commandeur et al. 2019) | Quantification of EAT | 776 | Non-contrast CT | DL: CNN | DSC = 0.871 |
| (Li et al. 2019) | Automatic pericardium segmentation | 53 | Non-contrast CT | DL: U-Net | AUC = 0.87 |
| (Aarthy et al. 2019) | Quantification of EAT | 20 | Non-contrast CT | DL: K mean clustering + CNN | CC = 0.803 |
| (Fulton et al. 2020) | Segmentation of EAT | 32 | Cardiac magnetic resonance imaging | DL: Neural network | DSC = 0.56 ± 0.12 |
| (Zhang et al. 2020) | Automatic epicardial fat segmentation and quantification | 20 | Non-contrast CT | DL: dual U-Nets + morphological processing layer |
CC = 0.93 DSC = 0.91 |
| (He et al. 2020a) | Automatic segmentation and quantification of EAT | 200 | CCTA | DL: 3D deep attention U-Net | DSC = 0.927 |
| (He et al. 2020b) | Automatic quantification of myocardium and pericardial fat | 422 | CCTA | DL: Deep attention U-Net |
ICC = 0.97 DSC = 0.88 |
| (Otaki et al. 2015) | Prediction of impaired myocardial blood flow from clinical and imaging data (EFV) | 85 | Non-contrast CT | ML: Ensemble-boosting logitboost algorithms |
AUC = 0.73 vs 0.67 (ML vs EFV) |
| (Rodrigues et al. 2017a) | Prediction of epicardial and mediastinal fat | 20 | Non-contrast CT | ML: Rotation forest + multi-layer perception regressor |
Predicting mediastinal fat based on EAT: CC = 0.986 RAE = 14.4% Predicting EAT based on mediastinal fat: CC = 0.928 RAE = 32.5% |
| (Commandeur et al. 2020) | Predict the long-term risk of MI and cardiac death based on clinical risk, CAC, and EAT | 1912 | Non-contrast CT | ML: XGBoost |
ML-AUC = 0.82 CAC-ACU = 0.77 ASCVD-AUC = 0.77 |
| (Tamarappoo et al. 2021) | The long-term prediction of hard cardiac events | 1069 | Non-contrast CT | ML: XGBoost |
ML-AUC = 0.81 CAC-AUC = 0.81 ASCVD-AUC = 0.74 |
| (Oikonomou et al. 2019) | Radiotranscriptomic signature of perivascular fat improves cardiac risk prediction | 1575 | CCTA | ML: Radiomics-RF |
For MACE discrimination: with radiomics signature-AUC = 0.88 without radiomics signature-AUC = 0.754 |
| (Lin et al. 2020) | Radiomics analysis of PCAT to distinguish patients with MI | 177 | CCTA | ML: XGBoost |
ML-AUC = 0.87 clinical features + PCAT attenuation-AUC = 0.77 clinical features alone-AUC = 0.76 |
CCTA Coronary computed tomography angiography, ML Machine learning, DL Deep learning, RF Random forest, CNN Convolutional neural network, XGBoost Extreme gradient boosting, EAT Epicardial adipose tissue, PCAT Pericoronary adipose tissue, EFV Epicardial fat volume (the volume of EAT), MI Myocardial infarction, CC Correlation coefficient, DSC Dice similarity coefficient, AUC Area under the ROC curve, MSE Mean square error, RAE Relative absolute error, ASCVD Atherosclerotic cardiovascular disease, CAC Coronary artery calcium, MACE Major adverse cardiovascular events