Table 4 Prior ROP AI study performance comparison
From: Automated identification of retinopathy of prematurity by image-based deep learning
Reference | Patients (N) | Cases | Images | Labels | Model | Specificity | Sensitivity | Accuracy |
|---|---|---|---|---|---|---|---|---|
Wang et al. [35] | 1273 | 3722 | 20,795 | normal/minor | Id-Net; Gr-Net | 99.32% (Id-Net); | 96.62% (Id-Net); 88.46% (Gr-Net) | – |
ROP/severe ROP | ||||||||
92.31% (Gr-Net) | ||||||||
Brown et al. [19] | 898 | 1762 | 5511 | normal/pre-plus | U-net (Inception-v1) | 94% (plus disease) | 93% (plus disease); 100% (pre-plus disease) | – |
disease/plus disease | ||||||||
94% (pre-plus disease) | ||||||||
Worrall et al. [34] | 35 | 347 | 1459 | normal/plus disease | GoogleNet; Bayesian CNNs | 0.983 (per image) | 0.825 (per image) | – |
0.954 (per exam) | ||||||||
0.947 (per exam) | ||||||||
Campbell et al. [37] | – | – | 77 | normal/pre-plus | i-ROP | – | – | 95% |
disease/plus disease | ||||||||
Hu et al. [38] | 720 | – | 3017 | normal/mild | Inception-v2; VGG-16; ResNet-50 | – | – | 0.970 (normal and ROP); |
ROP/severe ROP | ||||||||
0.840 (mild and severe) |