Diagnostic accuracy of feature prediction by deep learning based model as opposed to radiologist detected MRI findings
| Predicted feature n = 70 | Sensitivity | Specificity | PPV | NPV | Accuracy | |
|---|---|---|---|---|---|---|
| Hypointensity on T1WI | 86.36% | 0 | 93.44% | 0 | 81.43% | |
| Isointensity on T2WI | 63.16% | 84.62% | 94.74% | 34.37% | 67.14% | |
| Hyperintensity on FLAIR | 85.71% | 61.9% | 60% | 86.67% | 71.4% | |
| Haemorrhage | 54.17% | 78.26% | 56.62% | 76.6% | 70% | |
| Cyst | 79.07% | 59.26% | 75.56% | 64% | 71.4% | |
| Necrosis | 52.94% | 83.02% | 50% | 84.62% | 75.71% | |
| Enhancement heterogeneity | 81.36% | 72.73% | 94.12% | 42.11% | 80% | |
| Enhancement quantification | Mild | 50% | 92.86% | 82.35% | 73.58% | 75.71% |
| Moderate | 61.1% | 71.15% | 42.31% | 84.09% | 68.57% | |
| Severe | 83.3% | 84.78% | 74.07% | 90.70% | 84.29% | |
| Diffusion restriction | 53.33% | 96.77% | 94.12% | 68.16% | 75.4% | |
n: total number of abnormal cases which were picked by AS model and on which the mentioned features are predicted by AS model