Research Article
Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images
Table 2
The AUC of the machine learning models alone and in combination.
| Number | Cases | AUC (mean ± SD) |
| #1 | Disc fundus image (green channel) | 0.940 ± 0.039 | #2 | Disc RNFL thickness map | 0.942 ± 0.037 | #3 | Macular GCC thickness map | 0.944 ± 0.032 | #4 | Disc deviation map | 0.949 ± 0.030 | #5 | Macular deviation map | 0.952 ± 0.029 | #6 | Combination of #2 and #4 (images from disc OCT data) | 0.953 ± 0.032 | #7 | Combination of #3 and #5 (images from macular OCT data) | 0.954 ± 0.031 | #8 | Combination of #1, #2, and #4 (images from disc OCT data with fundus image) | 0.959 ± 0.031 | #9 | Combination of #1, #2, and #3 (automatically detected disc and macular center were not used in creating images) | 0.961 ± 0.029 | #10 | Combination of #2, #3, #4, and #5 (images from OCT data) | 0.963 ± 0.030 | #11 | Combination of all images | 0.963 ± 0.029 |
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