Table 4. Application of Automatic Coronary Calcium Scoring.
| Study | Year | Sensitivity (%), per Lesion | False Positives per CT Scan | ICC | CT Scans for Test (n) | Protocol of CT Scan | Method of Detection |
|---|---|---|---|---|---|---|---|
| Wolterink et al. [53] | 2016 | 79 | 0.2 | 0.96 | 530 | ECG-gated CT | 2.5D patch-based CNN (15 or 25 sizes from axial, coronal, sagittal planes) |
| Lessmann et al. [13] | 2018 | 91.2 | 40.7 mm3/scan | NA | 506 | Non-ECG-gated chest CT | Cascaded two 2.5D CNNs (CNN1 with large receptive field and CNN2 with smaller receptive field) |
| Cano-Espinosa et al. [54] | 2018 | NA | NA | 0.93* | 1000 | Non-ECG-gated chest CT | CNN based regression model, which directly predicts CAC score |
| Martin et al. [14] | 2020 | 93.2† | NA | 0.985 | 511 | ECG-gated CT | Two-fold deep-learning models, first one to exclude aorta, aortic valve, mitral valve regions; second one to classify coronary calcium voxels |
| van Velzen et al. [55] | 2020 | 93 | 4 mm3/scan | 0.99 | 529 | ECG-gated CT Non-EGC-gated chest CT (n = 3811) |
Same as Lessmann et al. 2018 [13] |
| Lee et al. [39] | 2021 | 93.3 | 0.11 | 0.99 | 2985 | ECG-gated CT | 3D patch-based CNN for semantic segmentation |
*Pearson correlation coefficient, †Per-patient sensitivity. CAC = coronary artery calcium score, CNN = convolutional neural network, CT = computed tomography, D = dimensional, ECG = electrocardiography, ICC = intraclass correlation coefficient, NA = not applicable