Table 3. Diagnostic Performance of Deep Learning (DL) Classifier Models for Glaucoma Detection.
| Model | Internal test data set (70 normal eyes, 70 glaucomatous eyes) | External test data set (150 normal eyes, 150 glaucomatous eyes) | ||||
|---|---|---|---|---|---|---|
| AUC (95% CI) | Sensitivity at 80% specificity | Sensitivity at 90% specificity | AUC (95% CI) | Sensitivity at 80% specificity | Sensitivity at 90% specificity | |
| cpRNFL (baseline)a | 0.92 (0.88-0.96) | 87.1 | 75.7 | 0.83 (0.79-0.87) | 61.3 | 40.7 |
| Real imagesb | 0.96 (0.94-0.99) | 95.7 | 88.6 | 0.84 (0.80-0.87) | 74.7 | 42 |
| Synthetic images, No.c | ||||||
| 1200 | 0.95 (0.92-0.97) | 91.4 | 84.3 | 0.86 (0.82-0.89) | 70.7 | 53.3 |
| 10 000 | 0.96 (0.94-0.98) | 94.3 | 90.0 | 0.88 (0.85-0.91) | 74.7 | 58.9 |
| 60 000 | 0.97 (0.94-0.99) | 98.6 | 94.3 | 0.87 (0.84-0.91) | 69.6 | 56.7 |
| 200 000 | 0.97 (0.95-0.99) | 95.7 | 91.4 | 0.90 (0.87-0.93) | 86.7 | 69.4 |
Abbreviations: AUC, area under the curve; cpRNFL, circumpapillary retinal fiber layer.
a
Model based on cpRNFL thickness.
b
DL model trained using only real images.
c
Four DL models trained using synthetic images of varying data set sizes.