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Related Experiment Video

Updated: May 24, 2025

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An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning.

Bo Zheng1,2, Chen Wang1, Maotao Zhang1

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

Translational Vision Science & Technology
|March 6, 2025
PubMed
Summary

This study introduces an intelligent grading model for myopic maculopathy using long-tail learning and an improved LTBSoftmax loss function. The model enhances diagnostic accuracy and efficiency, offering a practical tool for clinicians.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Myopic maculopathy presents a long-tail data distribution challenge in automated grading.
  • Existing models may struggle with data imbalance, impacting diagnostic accuracy and efficiency.

Purpose of the Study:

  • To develop an intelligent grading model for myopic maculopathy using a long-tail learning framework.
  • To address data imbalance with an improved LTBSoftmax loss function for enhanced grading capability and efficiency.

Main Methods:

  • A dataset of 7529 color fundus photographs was utilized, with expert ophthalmologist annotations for ground truth.
  • An intelligent grading model was constructed incorporating the LTBSoftmax loss function and ND Block for enhanced feature extraction.
  • The model's performance was evaluated using standard grading metrics.

Main Results:

  • The LTBSoftmax model achieved a κ coefficient of 88.89% in diagnosing four types of myopic maculopathy.
  • The model demonstrates superior performance with high agreement with expert diagnoses.
  • The model's compact size (18.7 MB) indicates high efficiency in storage and computational resources.

Conclusions:

  • The intelligent grading system effectively improves myopic maculopathy classification using long-tailed learning strategies.
  • This model serves as a practical grading tool for clinicians, especially in resource-limited settings.