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Automatic Screening and Identifying Myopic Maculopathy on Optical Coherence Tomography Images Using Deep Learning.

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Deep learning models were engineered to detect myopic maculopathy in high myopia patients using optical coherence tomography (OCT) images. The artificial intelligence system demonstrated high accuracy, comparable to junior retinal specialists.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • High myopia is a leading cause of vision impairment.
  • Myopic maculopathy encompasses various pathological changes in the macula.
  • Accurate detection of myopic maculopathy is crucial for timely intervention.

Purpose of the Study:

  • To develop and validate deep learning (DL) models for identifying myopic maculopathy.
  • To utilize optical coherence tomography (OCT) images for AI-driven diagnosis.
  • To assess the performance of DL models against human experts.

Main Methods:

  • An AI system was trained on 2342 OCT macular images from 1041 patients with pathologic myopia.
  • A ResNeSt101 architecture was employed to develop five independent models for specific maculopathies.
  • The system was tested on 450 images from 297 high myopia patients, using focal loss and Youden Index for optimization.

Main Results:

  • The AI system achieved high diagnostic performance with Area Under the Curve (AUC) values ranging from 0.927 to 0.974.
  • Sensitivities of the AI system were comparable to or exceeded those of junior retinal specialists (56.16-99.73%).
  • The system provides interpretable visual explanations through heatmaps.

Conclusions:

  • A CNN-based DL AI system was successfully developed for detecting and classifying myopic maculopathy using OCT images.
  • The AI system demonstrated performance on par with or superior to junior retinal specialists.
  • The AI system holds potential for large-scale high myopia screening programs.