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Development of a deep learning algorithm for myopic maculopathy classification based on OCT images using transfer

Xiaoying He1, Peifang Ren2, Li Lu3

  • 1Department of Ophthalmology, Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.

Frontiers in Public Health
|October 10, 2022
PubMed
Summary

Deep learning with transfer learning accurately classifies myopic maculopathy (MM) using optical coherence tomography (OCT) images. This automated system shows promise for clinical applications in diagnosing MM.

Keywords:
ATN classification systemartificial intelligencedeep learningmyopia maculopathyoptical coherence tomographypathologic myopia

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Myopic maculopathy (MM) classification is crucial for patient management.
  • Accurate diagnosis relies on interpreting optical coherence tomography (OCT) images.
  • Existing methods may lack efficiency and consistency.

Purpose of the Study:

  • To develop an automatic intelligent classification system for myopic maculopathy (MM).
  • To apply deep learning (DL) and transfer learning (TL) techniques to macular OCT images.
  • To identify specific types of MM using an automated approach.

Main Methods:

  • Retrospective analysis of 3,400 macular OCT images from 2,866 myopic patients.
  • Training two DL algorithms: one from scratch (A) and one using TL (B).
  • Evaluating performance using sensitivity, specificity, accuracy, kappa, and AUC; including human-machine comparison.

Main Results:

  • Algorithm B (TL) demonstrated superior performance during training.
  • Algorithm B achieved high accuracy (96.04%) and AUC (0.986) in the test set.
  • The automated system performed comparably to ordinary ophthalmologists in human-machine comparison.

Conclusions:

  • The TL-based DL algorithm shows excellent performance for classifying MM from OCT images.
  • The developed automatic diagnosis system has potential clinical application prospects.
  • This AI-driven approach can aid in the efficient and accurate diagnosis of myopic maculopathy.