Table 3 Group separation performance for hippocampus volume and the convolutional neural network models
Sample | Hippocampus volume (residuals) | 3D convolutional neural network | ||
|---|---|---|---|---|
Balanced accuracy (mean ± SD) | AUC | Balanced accuracy (mean ± SD) | AUC (mean ± SD) | |
ADNI-GO/2 | ||||
MCI vs. CN | (70.0% ± 6.8%) | (0.773 ± 0.091) | (74.5% ± 6.2%) | (0.785 ± 0.078) |
AD vs. CN | (84.4% ± 3.6%) | (0.945 ± 0.024) | (88.9% ± 5.3%) | (0.949 ± 0.029) |
MCI+ vs. CN− | (75.6% ± 7.1%) | (0.831 ± 0.080) | (86.7% ± 10.3%) | (0.925 ± 0.071) |
AD+ vs. CN− | (86.2% ± 4.2%) | (0.954 ± 0.025) | (94.9% ± 3.8%) | (0.985 ± 0.017) |
ADNI-3 | ||||
MCI vs. CN | 62.8% (63.1% ± 1.4%) | 0.683 | 63.1% (63.6% ± 1.5%) | 0.684 (0.677 ± 0.020) |
AD vs. CN | 83.4% (83.4% ± 0.4%) | 0.917 | 84.4% (81.7% ± 2.9%) | 0.913 (0.899 ± 0.013) |
MCI+ vs. CN− | 69.1% (69.2% ± 2.7%) | 0.791 | 69.8% (68.3% ± 4.4%) | 0.810 (0.742 ± 0.024) |
AD+ vs. CN− | 83.6% (82.0% ± 1.8%) | 0.882 | 80.2% (75.5% ± 4.2%) | 0.830 (0.828 ± 0.028) |
AIBL | ||||
MCI vs. CN | 67.4% (67.6% ± 0.5%) | 0.741 | 68.2% (67.3% ± 2.7%) | 0.763 (0.749 ± 0.012) |
AD vs. CN | 84.1% (85.3% ± 1.5%) | 0.927 | 85.0% (82.3% ± 3.0%) | 0.950 (0.926 ± 0.007) |
MCI+ vs. CN− | 78.5% (78.8% ± 0.9%) | 0.874 | 75.4% (73.6% ± 3.1%) | 0.828 (0.814 ± 0.022) |
AD+ vs. CN− | 87.2% (89.1% ± 2.4%) | 0.976 | 88.3% (85.3% ± 3.3%) | 0.978 (0.958 ± 0.011) |
DELCODE | ||||
MCI vs. CN | 69.0% (69.0% ± 9.6%) | 0.774 | 71.0% (69.7% ± 2.6%) | 0.775 (0.772 ± 0.017) |
AD vs. CN | 88.4% (86.4% ± 3.0%) | 0.943 | 85.5% (80.5% ± 4.0%) | 0.953 (0.938 ± 0.013) |
MCI+ vs. CN− | 77.4% (77.8% ± 0.7%) | 0.867 | 72.2% (74.9% ± 3.5%) | 0.840 (0.830 ± 0.017) |
AD+ vs. CN− | 88.2% (87.6% ± 1.8%) | 0.954 | 83.3% (82.2% ± 4.0%) | 0.968 (0.956 ± 0.012) |