iScience (Nov 2021)

Comparison of deep learning systems and cornea specialists in detecting corneal diseases from low-quality images

  • Zhongwen Li,
  • Jiewei Jiang,
  • Wei Qiang,
  • Liufei Guo,
  • Xiaotian Liu,
  • Hongfei Weng,
  • Shanjun Wu,
  • Qinxiang Zheng,
  • Wei Chen

Journal volume & issue
Vol. 24, no. 11
p. 103317

Abstract

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Summary: The 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.

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