Skip to main content

ORIGINAL RESEARCH article

Front. Med.
Sec. Ophthalmology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1492808
This article is part of the Research Topic Imaging in the Diagnosis and Treatment of Eye Diseases View all 3 articles

Effective automatic classification methods via deep learning for myopic maculopathy

Provisionally accepted
Zheming Zhang Zheming Zhang 1Qi Gao Qi Gao 2,3Dong Fang Dong Fang 4Alfira Mijit Alfira Mijit 1Lu Chen Lu Chen 4*Wangting Li Wangting Li 4*Yantao Wei Yantao Wei 1*
  • 1 State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong Province, China
  • 2 School of Future Technology, South China University of Technology, Guangzhou, China
  • 3 Pazhou Lab, Guangzhou, China
  • 4 Shenzhen Eye Hospital, Jinan University, Shenzhen, China

The final, formatted version of the article will be published soon.

    Pathologic myopia (PM) associated with myopic maculopathy (MM) is a significant cause of visual impairment, especially in East Asia, where its prevalence has surged. Early detection and accurate classification of myopia-related fundus lesions are critical for managing PM. Traditional clinical analysis of fundus images is time-consuming and dependent on specialist expertise, driving the need for automated, accurate diagnostic tools.This study developed a deep learning-based system for classifying five types of MM using color fundus photographs. Five architectures-ResNet50, EfficientNet-B0, Vision Transformer (ViT), Contrastive Language-Image Pre-Training (CLIP), and RETFound-were utilized. An ensemble learning approach with weighted voting was employed to enhance model performance. The models were trained on a dataset of 2,159 annotated images from Shenzhen Eye Hospital, with performance evaluated using accuracy, sensitivity, specificity, F1-Score, Cohen's Kappa, and area under the receiver operating characteristic curve (AUC). The ensemble model achieved superior performance across all metrics, with an accuracy of 95.4% (95% CI: 93.0% -97.0%), sensitivity of 95.4% (95% CI: 86.8% -97.5%), specificity of 98.9% (95% CI: 97.1% -99.5%), F1-Score of 95.3% (95% CI: 93.2% -97.2%), Kappa value of 0.976 (95% CI: 0.957 -0.989), and AUC of 0.995 (95% CI: 0.992 -0.998). The voting ensemble method demonstrated robustness and high generalization ability in classifying complex lesions, outperforming individual models.The ensemble deep learning system significantly enhances the accuracy and reliability of MM classification. This system holds potential for assisting ophthalmologists in early detection and precise diagnosis, thereby improving patient outcomes. Future work could focus on expanding the dataset, incorporating image quality assessment, and optimizing the ensemble algorithm for better efficiency and broader applicability.

    Keywords: myopic maculapathy, ensemble learning, deep learning, artificial intelligence, fundus image

    Received: 08 Sep 2024; Accepted: 28 Oct 2024.

    Copyright: © 2024 Zhang, Gao, Fang, Mijit, Chen, Li and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Lu Chen, Shenzhen Eye Hospital, Jinan University, Shenzhen, China
    Wangting Li, Shenzhen Eye Hospital, Jinan University, Shenzhen, China
    Yantao Wei, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, Guangdong Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.