Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study.

Yueye Wang Xiaotong Han Cong Li Lixia Luo Qiuxia Yin Jian Zhang Guankai Peng Danli Shi Mingguang He

Published in: Journal of medical Internet research (2024)

Label errors based on human image grading, although in a small percentage, can significantly affect the performance evaluation of DL algorithms in real-world DR screening.

Keyphrases

deep learning
diabetic retinopathy
machine learning
endothelial cells
optical coherence tomography
artificial intelligence
patient safety
convolutional neural network
adverse drug
emergency department
quality improvement