Table 5.
Performance of artificial intelligence models 1–3 on the test set
| Group | Accuracy (95% CI) | AUC (95% CI) | Sensitivity, % (95% CI) | Specificity, % (95% CI) | PPV (95% CI) |
|---|---|---|---|---|---|
| AI model 1 | |||||
| Normal (n=84) | 0.934 (0.882–0.965) | 0.934 (0.897–0.972) | 90.5 (84.7–94.3) | 96.4 (92.0–98.5) | 0.962 (0.917–0.984) |
| Subclinical KC (n=15) | 0.910 (0.854–0.947) | 0.831 (0.713–0.948) | 73.3 (65.8–79.7) | 92.8 (87.4–96.0) | 0.500 (0.422–0.578) |
| KC (n=68) | 0.964 (0.920–0.985) | 0.961 (0.929–0.992) | 94.1 (89.1–97.0) | 98.0 (94.1–99.4) | 0.970 (0.927–0.989) |
| Weighted average overall (n=167) | 0.947 | 0.938 | 90.8 | 96.9 | 0.924 |
| Macro average overall (n=167) | 0.936 | 0.968 | 86.0 | 95.7 | 0.811 |
| AI model 2 | |||||
| Normal (n=33) | 0.984 (0.902–0.999) | 1.000 (1.000–1.000) | 100.0 (92.7–100.0) | 96.6 (87.5–99.4) | 0.971 (0.882–0.995) |
| Subclinical KC (n=4) | 0.952 (0.856–0.987) | 0.823 (0.815–0.831) | 25.0 (15.3–37.9) | 100.0 (92.7–100.0) | 1.000 (0.927–1.000) |
| KC (n=25) | 0.935 (0.835–0.979) | 0.994 (0.993–0.994) | 96.0 (86.8–99.1) | 91.9 (81.4–97.0) | 0.889 (0.777–0.951) |
| Weighted average overall (n=62) | 0.956 | 0.985 | 93.0 | 94.3 | 0.929 |
| Macro average overall (n=62) | 0.957 | 0.939 | 73.7 | 96.1 | 0.953 |
| AI model 3 | |||||
| Normal (n=33) | 0.984 (0.902–0.999) | 1.000 (1.000–1.000) | 100.0 (92.7–100.0) | 96.6 (87.5–99.4) | 0.971 (0.882–0.995) |
| Subclinical KC (n=4) | 0.952 (0.856–0.987) | 0.901 (0.897–0.905) | 25.0 (15.3–37.9) | 100.0 (92.7–100.0) | 1.000 (0.927–1.000) |
| KC (n=25) | 0.935 (0.835–0.979) | 0.995 (0.994–0.995) | 96.0 (86.8–99.1) | 91.9 (81.4–97.0) | 0.889 (0.777–0.951) |
| Weighted average overall (n=62) | 0.956 | 0.991 | 93.0 | 94.3 | 0.929 |
| Macro average overall (n=62) | 0.957 | 0.965 | 73.7 | 96.1 | 0.953 |
KC: Keratoconus, n: Number of images, AUC: Area under the receiver operating characteristic curve, PPV: Positive predictive value, AI: Artificial intelligence, CI: Confidence interval