Comparison of deep learning systems and cornea specialists in detecting corneal diseases from low-quality images
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 unkno...
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| Auteurs principaux: | , , , , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Elsevier
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/9b93422a7e1e42fcba8ab69db7af6fd6 |
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| Résumé: | 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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