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Preventing corneal blindness caused by keratitis using artificial intelligence

Author

Listed:
  • Zhongwen Li

    (Wenzhou Medical University
    Wenzhou Medical University)

  • Jiewei Jiang

    (Xi’an University of Posts and Telecommunications)

  • Kuan Chen

    (Wenzhou Medical University)

  • Qianqian Chen

    (Wenzhou Medical University)

  • Qinxiang Zheng

    (Wenzhou Medical University
    Wenzhou Medical University)

  • Xiaotian Liu

    (Wenzhou Medical University)

  • Hongfei Weng

    (Wenzhou Medical University)

  • Shanjun Wu

    (Wenzhou Medical University)

  • Wei Chen

    (Wenzhou Medical University
    Wenzhou Medical University)

Abstract

Keratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

Suggested Citation

  • Zhongwen Li & Jiewei Jiang & Kuan Chen & Qianqian Chen & Qinxiang Zheng & Xiaotian Liu & Hongfei Weng & Shanjun Wu & Wei Chen, 2021. "Preventing corneal blindness caused by keratitis using artificial intelligence," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24116-6
    DOI: 10.1038/s41467-021-24116-6
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