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Research on an artificial intelligence-based myopic maculopathy grading method using EfficientNet.

Bo Zheng1,2, Maotao Zhang1, Shaojun Zhu1,2

  • 1School of Information Engineering, Huzhou University, Huzhou, China.

Indian Journal of Ophthalmology
|December 22, 2023
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Summary

An artificial intelligence model using EfficientNet accurately grades myopic maculopathy from fundus images. This AI tool aids ophthalmologists in the early diagnosis of various stages of myopic maculopathy.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Myopic maculopathy grading is crucial for timely intervention.
  • Current grading methods can be subjective and time-consuming.
  • Delayed diagnosis of myopic maculopathy can lead to vision impairment.

Purpose of the Study:

  • To develop an AI-based grading system for myopic maculopathy using EfficientNet.
  • To improve the efficiency and accuracy of diagnosing different degrees of myopic maculopathy.
  • To overcome limitations in current diagnostic workflows for myopic maculopathy.

Main Methods:

  • Trained EfficientNet models (B0-B7) on 4642 color fundus photographs.
  • Compared EfficientNet models against VGG16 and ResNet50.
  • Evaluated models using sensitivity, specificity, F1 score, AUC, kappa value, and accuracy.

Main Results:

  • The EfficientNet-B0 model achieved the highest kappa value (88.32%) and accuracy (83.58%).
  • High sensitivity was observed for diagnosing tessellated fundus (96.86%) and macular atrophy (88.75%).
  • Specificities exceeded 93% for all conditions, with AUCs reaching 0.992 for tessellated fundus.

Conclusions:

  • EfficientNet models can effectively grade myopic maculopathy from fundus images.
  • The AI models can differentiate between healthy fundi and four degrees of myopic maculopathy.
  • This AI tool shows potential to assist ophthalmologists in preliminary myopic maculopathy diagnosis.