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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)
  1. ROP = retinopathy of prematurity; GoogleNet = google inception net; CNN = convolutional neural network; VGG = visual geometry group; ResNet = residual network