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Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images.

Li Lu1, Enliang Zhou2, Wangshu Yu1

  • 1Department of Ophthalmology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.

Communications Biology
|October 27, 2021
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Summary
This summary is machine-generated.

Deep learning systems can now detect pathological myopia (PM) and related eye lesions using fundus images. This technology offers accurate, efficient screening for widespread myopia control.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Myopia is a global epidemic, with high and pathological myopia (PM) causing significant visual impairment and blindness in China.
  • Current myopia screening methods are time-consuming and labor-intensive, necessitating automated solutions.

Purpose of the Study:

  • To design and evaluate deep learning systems for detecting pathological myopia (PM) and myopic macular lesions.
  • To classify these conditions based on the international META-PM classification system using color fundus images.

Main Methods:

  • Development of a series of deep learning systems.
  • Training and validation using color fundus images.
  • Performance evaluation against the META-PM classification system.

Main Results:

  • The deep learning systems demonstrated robust performance on both test and external validation datasets.
  • The system's diagnostic accuracy was comparable to that of general ophthalmologists and retinal specialists.

Conclusions:

  • Deep learning offers a viable solution for automated detection of pathological myopia and associated lesions.
  • Widespread adoption of this technology can enable effective, large-scale myopia screening programs.