Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives

Area of Science:

• Ophthalmology
• Artificial Intelligence
• Machine Learning

Background:

• Glaucoma diagnosis and management are complex due to varied presentations and lack of a consensus definition.
• Developing artificial intelligence (AI) models for glaucoma screening is crucial for early detection but faces significant data challenges.
• Centralizing large, diverse datasets for AI model training is hindered by privacy, regulatory, and data sharing concerns.

Purpose of the Study:

• To review the application of Federated Learning (FL) in training AI models for glaucoma screening.
• To explore how FL addresses data sharing and privacy challenges in ophthalmic AI development.
• To assess the potential of FL in overcoming obstacles in AI-driven glaucoma detection.

Main Methods:

• A comprehensive literature review was conducted on FL in AI for glaucoma screening.
• Searched PubMed and IEEE Xplore databases (1950-2024) using keywords like "glaucoma," "federated learning," and "artificial intelligence."
• Included articles discussing FL in glaucoma AI or data sharing/privacy in ophthalmic AI.

Main Results:

• Federated Learning enables collaborative AI model development without centralizing sensitive patient data, addressing privacy and regulatory issues.
• FL improves model performance and generalizability by utilizing diverse datasets while ensuring data security.
• FL models demonstrate comparable or superior accuracy to centralized training methods in real-world settings.

Conclusions:

• Federated Learning offers a promising strategy to overcome obstacles in developing AI models for glaucoma screening.
• FL balances the need for extensive, diverse training data with patient privacy and regulatory compliance.
• This approach facilitates collaborative model training, leading to more accurate and generalizable AI solutions for glaucoma detection.