Table 2 Performance of previously established AI diagnostic systems in images of varying quality.

From: Deep learning from “passive feeding” to “selective eating” of real-world data

 

Zhongshan ophthalmic centre dataset

Xudong ophthalmic hospital dataset

GON

RED

LDRB

GON

RED

Sensitivity (95% CI)

Specificity (95% CI)

Sensitivity (95% CI)

Specificity (95% CI)

Sensitivity (95% CI)

Specificity (95% CI)

Sensitivity (95% CI)

Specificity (95% CI)

Sensitivity (95% CI)

Specificity (95% CI)

Good and poor quality (without DLIFS)

88.6% (85.1–92.1)

95.7% (94.6–96.8)

89.1% (85.5–92.7)

94.0% (92.7–95.3)

86.6% (81.9–91.3)

94.6% (93.8–95.4)

90.3% (87.3–93.3)

97.9% (97.3–98.5)

85.1% (80.8–89.4)

97.3% (96.7–97.9)

Good quality only (with DLIFS)

98.1% (96.4–98.8)

96.6% (95.5–97.7)

94.9% (92.1–97.7)

95.1% (93.8–96.4)

96.2% (93.2–99.2)

97.6% (97.0–98.2)

98.0% (96.4–99.6)

98.5% (98.0–99.0)

95.8% (92.9–98.7)

98.0% (97.4–98.6)

Poor quality only

52.2% (40.2–64.2)

90.3% (85.9–94.7)

67.2% (55.4–79.0)

87.9% (83.1–92.7)

53.3% (38.7–67.9)

67.3% (61.7–72.9)

58.1% (46.9–69.3)

92.6% (89.3–95.9)

59.0% (48.1–69.9)

90.0% (86.2–93.8)

  1. AI artificial intelligence, DLIFS deep learning-based image filtering system, GON glaucomatous optic neuropathy, RED retinal exudation/drusen, LDRB lattice degeneration/retinal breaks.