Table 1.
Confusion matrix results are provided for discrimination of rotational and translational instability by the three algorithms (triplanar concatenated, LSTM-based recurrent RNN, and 3D encoder method) and the independent radiologist reader
| Class | Confusion matrix results | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model | TP | TN | FP | FN | Sensitivity (recall/TPR) | Specificity (TNR) |
Precision (PPV) | NPV | Miss rate (FNR) | Fall-out rate (FPR) | FDR | FOR | Accuracy | F1 | DOR | |
| Triplanar parallel concatenated ResNeXt50 | Rotational instability | 162 | 114 | 45 | 52 | 75.7% | 71.7% | 78.3% | 68.7% | 24.3% | 28.3% | 21.7% | 31.3% | 74.0% | 0.77 | 7.9 |
| Translational instability | 48 | 269 | 19 | 37 | 56.5% | 93.4% | 71.6% | 87.9% | 43.5% | 6.6% | 28.4% | 12.1% | 85.0% | 0.63 | 18.4 | |
| ResNeXt50 + LSTM-based RNN | Rotational instability | 137 | 120 | 39 | 77 | 64.0% | 75.5% | 77.8% | 60.9% | 36.0% | 24.5% | 22.2% | 39.1% | 68.9% | 0.70 | 5.5 |
| Translational instability | 38 | 278 | 10 | 47 | 44.7% | 96.5% | 79.2% | 85.5% | 55.3% | 3.5% | 20.8% | 14.5% | 84.7% | 0.57 | 22.5 | |
| 3D-ResNet50 encoder method | Rotational instability | 61 | 147 | 12 | 153 | 28.5% | 92.5% | 83.6% | 49.0% | 71.5% | 7.5% | 16.4% | 51.0% | 55.8% | 0.43 | 4.9 |
| Translational instability | 32 | 273 | 15 | 53 | 37.6% | 94.8% | 68.1% | 83.7% | 62.4% | 5.2% | 31.9% | 16.3% | 81.8% | 0.48 | 11.0 | |
| Independent radiologist reading | Rotational instability | 174 | 112 | 47 | 40 | 81.3% | 70.4% | 78.7% | 73.7% | 18.7% | 29.6% | 21.3% | 26.3% | 76.7% | 0.80 | 10.4 |
| Translational instability | 68 | 212 | 76 | 17 | 80.0% | 73.6% | 47.2% | 92.6% | 20.0% | 26.4% | 52.8% | 7.4% | 75.1% | 0.59 | 11.2 | |