Related Experiment Video
Updated: Aug 7, 2026
07:20
Inducement and Evaluation of a Murine Model of Experimental Myopia
Published on: January 22, 2019
A federal learning-driven artificial intelligence framework for fundus image myopia diagnosis
Xiaolong Yin1, Chunhong Yu1, Weiwei Xiong1
1Ophthalmology Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
Digital Health
|August 6, 2026
Summary
A new privacy-preserving federated learning (FL) framework accurately classifies fundus images for myopia and pathological myopia. This approach ensures data privacy and generalizes well across different institutions, addressing key public health challenges in ophthalmology.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Myopia is a significant global public health concern.
- Accurate classification of fundus images is crucial for diagnosing myopia and related pathologies.
- Existing methods face challenges with data privacy, heterogeneity, and class imbalance in multi-institutional settings.
Purpose of the Study:
- To develop a privacy-preserving federated learning (FL) framework for the triple classification of fundus images (normal, myopia, pathological myopia).
- To ensure the framework generalizes across institutions despite data heterogeneity and class imbalance.
- To maintain data privacy during collaborative model training.
Main Methods:
- Proposed a novel FL framework incorporating a genetic algorithm-inspired dynamic aggregator (FedProx_GA), a distance-aware attention module (OptiFocus), and a class-frequency dynamic loss.
- Trained and evaluated the framework on 1,279 fundus images from three heterogeneous medical centers.
- Compared performance against standard FL baselines using metrics like AUC, accuracy, sensitivity, and specificity.
Main Results:
- Achieved a high Area Under the Curve (AUC) of 0.9889, nearing the performance of centralized data processing.
- Significantly outperformed conventional FL methods in classifying fundus images.
- Demonstrated robust cross-center generalization with high sensitivity (0.9346) and specificity (0.9673), effectively handling data heterogeneity and class imbalance without compromising privacy.
Conclusions:
- The developed FL framework offers an effective, privacy-preserving solution for collaborative ophthalmic AI.
- The framework shows strong potential for multi-institutional clinical deployment in diagnosing myopia.
- Future research should involve prospective validation with larger, diverse cohorts.
