Equitable artificial intelligence for glaucoma screening with fair identity normalization

Min Shi

doi:10.1038/s41746-025-01432-5

2025-01-20

DOI：10.1038/s41746-025-01432-5

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En 中文

青光眼是全球不可逆性失明的首要原因。研究表明，青光眼对种族和少数民族群体具有不成比例的影响。现有的青光眼检测深度学习模型可能无法在多样化的身份群体中实现公平的性能。我们开发了公平身份归一化（FIN）模块，以平衡不同身份群体间的特征重要性，从而提升模型性能的公平性。光学相干断层扫描（OCT）测量结果用于将患者分为青光眼和非青光眼两组。采用公平性缩放的受试者工作特征曲线下面积（ES-AUC）来量化模型性能的公平性。在引入针对种族群体的FIN后，总体AUC和ES-AUC分别从0.82提升至0.85和从0.77提升至0.81，其中黑人的AUC从0.77提升至0.82。在引入针对民族群体的FIN后，总体AUC和ES-AUC分别从0.82提升至0.84和从0.77提升至0.80，其中西班牙裔的AUC从0.75提升至0.79。

Keyword:

OPEN-ANGLE GLAUCOMA

VISUAL-FIELD

OPTIC DISC

PREVALENCE

INTERPOLATION

BURDEN

期刊

npj Digital Medicine

IF:

15.1

论文数:

3.3K

被引数:

1.5W

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