Federated Learning in Ocular Imaging: Current Progress and Future Direction.

Area of Science:

• Ophthalmology
• Artificial Intelligence
• Medical Imaging

Background:

• Deep learning (DL) significantly impacts ocular imaging, aiding in the detection and classification of eye diseases using retinal photographs, OCT, and OCT-angiography.
• Traditional DL training necessitates pooling extensive datasets, raising concerns about data security and patient privacy.

Purpose of the Study:

• To introduce federated learning (FL) as a privacy-preserving alternative for AI in ocular imaging.
• To review current applications of FL in the field of ocular imaging.
• To discuss challenges and future prospects of FL in ophthalmology.

Main Methods:

• Review of existing literature on federated learning applications in ocular imaging.
• Conceptual explanation of federated learning principles and its advantages over centralized training.

Main Results:

• Federated learning facilitates collaborative AI model training without direct data sharing, mitigating privacy risks.
• FL demonstrates potential for robust and generalizable AI models in ocular diagnostics.

Conclusions:

• Federated learning presents a promising solution for privacy-preserving AI development in ocular imaging.
• FL can overcome data sharing barriers, fostering wider adoption of AI tools in ophthalmology.