Table 1.
Confusion matrices showing the proportion of actual states reclassified by the AI models and represented by the predicted states
| A.Testing tomographic changes AI | ||
|---|---|---|
|
| ||
| Model A | Predicted | |
|
| ||
| Progression | No progression | |
| Actual | ||
| Progression | 75% (0.73±0.13) | 25% (0.34±0.15) |
| No progression | 4.5% (0.98±0) | 95.5% (0.13±0.13) |
|
| ||
| B. Clinical risk factors AI | ||
|
| ||
| Model B | Predicted | |
|
| ||
| Progression | No progression | |
|
| ||
| Predictions from Model A | ||
| Progression | 76.4% (0.77±0.15) | 23.6% (0.32±0.12) |
| No progression | 32.9% (0.64±0.12) | 67.1% (0.3±0.12) |
AI=artificial intelligence, RF=random forest. Model-A: testing changes in tomographic parameters on previously reported RF model.[11] Model-B: predicted results from Model-A used to evaluate clinical risk factors. The mean±standard deviation of the respective RF classifier probability scores is mentioned in brackets