Table 3.
Sub-cohort analyses to test performance of the two convolutional neural networks models by type of computed tomography scanner, noncontrast versus contrast scans, and exclusion of native kidneys from patients who have undergone a transplant
| Model | ||
|---|---|---|
| 2D global slice-by-slice model | ||
| Training set: Philips and GE scanner | Validation set: Siemens scanner | Correctly predicted normal or severe fibrosis |
| All data (n=320) | All data (n=53) | 49/53 (92%) |
| Exclude contrast CT scans (n=286) | Exclude contrast CT scans (n=43) | 40/43 (93%) |
| Exclude contrast CT scans, exclude native kidneys in transplant patients (n=159) | Exclude contrast CT scans, exclude native kidneys in transplant patients (n=29) | 26/29 (90%) |
| 2D U-Net voxel-based model | ||
| Training set: Philips and GE scanner | Validation set: Siemens scanner | Correctly predicted normal or severe fibrosis |
| All data (n=320) | All data (n=53) | 49/53 (92%) |
| Exclude contrast CT scans (n=286) | Exclude contrast CT scans (n=43) | 37/43 (86%) |
| Exclude contrast CT scans, exclude native kidneys in transplant patients (n=159) | Exclude contrast CT scans, exclude native kidneys in transplant patients (n=29) | 26/29 (90%) |
2D, two-dimensional; CT, computed tomography.