Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence

Area of Science:

• Ophthalmic diagnostics
• Medical Artificial Intelligence
• Machine Learning

Background:

• Scarcity of annotated datasets hinders ophthalmic disease detection.
• Need for improved early detection of eye conditions.
• Generative Adversarial Networks (GANs) offer potential for data synthesis.

Purpose of the Study:

• To explore GAN applications in ophthalmic diagnostics.
• To enhance synthetic ophthalmic image quality for training diagnostic algorithms.
• To address limitations in current ophthalmic dataset availability.

Main Methods:

• Systematic review of literature from January to April 2024.
• Searches conducted on PubMed, Embase, and Scopus.
• Selection criteria focused on GANs for retinal and OCT image generation and diagnostic improvement.

Main Results:

• GANs successfully generate high-resolution retinal and OCT images.
• Models like DR-GAN and Pix2Pix create realistic synthetic data.
• GAN-generated images improve training for algorithms detecting diabetic retinopathy, glaucoma, and AMD.

Conclusions:

• GANs significantly advance ophthalmic diagnostics by providing synthetic images.
• Challenges include dataset size, interpretability, and noise reduction.
• Future work should optimize GANs and integrate multi-modal data for enhanced accuracy.