摘要：SummaryThe performance of deep learning in disease detection from high-quality clinical images is identical to and even greater than that of human doctors. However, in low-quality images, deep learning performs poorly. Whether human doctors also have poor performance in low-quality images is unknown. Here, we compared the performance of deep learning systems with that of cornea specialists in detecting corneal diseases from low-quality slit lamp images. The results showed that the cornea specialists performed better than our previously established deep learning system (PEDLS) trained on only high-quality images. The performance of the system trained on both high- and low-quality images was superior to that of the PEDLS while inferior to that of a senior corneal specialist. This study highlights that cornea specialists perform better in low-quality images than the system trained on high-quality images. Adding low-quality images with sufficient diagnostic certainty to the training set can reduce this performance gap.Graphical abstractDisplay OmittedHighlights•Deep learning performs poorly in low-quality images for detecting corneal diseases•Corneal specialists perform better than the PEDLS in low-quality images•The performance of the NDLS is better than that of the PEDLS in low-quality images•Adding low-quality images to the training set can improve the system's performanceOcular surface; Ophthalmology; Artificial intelligence
