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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.

Mouayad Masalkhi^1,2, Kyle Sporn³, Rahul Kumar⁴

¹University College Dublin School of Medicine, Dublin, Belfield, Ireland. Mouayad.masalkhi1@gmail.com.

Journal of Imaging Informatics in Medicine

|May 20, 2025

Summary

Generative adversarial networks (GANs) create synthetic ophthalmic images to improve AI diagnostics. This approach addresses limited data for training algorithms to detect eye diseases like diabetic retinopathy.

Keywords:

Diffusion models GAN Generative adversarial networks Image synthesis OCT

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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.