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

Journal: Journal of imaging informatics in medicine
Published Date: May 20, 2025

Abstract

Generative adversarial networks (GANs), introduced by Ian Goodfellow in 2014, have revolutionized adversarial machine learning, particularly in data synthesis. This manuscript explores their application in ophthalmic diagnostics, addressing the scarcity of annotated datasets and the need for improved early disease detection. By leveraging GAN architectures, the goal is to enhance the quality of synthetic ophthalmic images, ultimately improving diagnostic algorithm training. A systematic review was conducted from January to April 2024 across PubMed, Embase, and Scopus. Search terms included "Generative Adversarial Networks" and "ophthalmic image synthesis." Articles were selected based on relevance to retinal image generation and diagnostic improvement in ophthalmology. GANs show considerable promise in generating high-resolution retinal and optical coherence tomography (OCT) images. Models like DR-GAN and Pix2Pix have successfully synthesized images that resemble real diagnostic data, proving valuable when annotated datasets are scarce. GAN-generated images enhance training for algorithms detecting diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration. Recent advances, including conditional GANs and CycleGANs, have enabled disease-specific image generation, boosting the diversity of training datasets, particularly in resource-limited settings. Integrating GANs into ophthalmic diagnostics represents a significant leap in medical AI, offering high-quality synthetic images to improve diagnostic algorithms. Despite their potential, challenges such as the need for larger datasets, improved image interpretability, and noise reduction must be addressed. Future research should focus on optimizing these models and incorporating multi-modal data to enhance diagnostic accuracy in clinical settings.

Authors

  • Mouayad Masalkhi
    University College Dublin School of Medicine, Belfield, Dublin 4, Ireland.
  • Kyle Sporn
    Norton College of Medicine, SUNY Upstate Medical University, Syracuse, NY 13210, USA.
  • Rahul Kumar
  • Joshua Ong
  • Tuan Nguyen
  • Ethan Waisberg
    University College Dublin School of Medicine, Belfield, Dublin, Ireland. Electronic address: ethan.waisberg@ucdconnect.ie.
  • Nasif Zaman
    Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada, United States.
  • Andrew G Lee
    Center for Space Medicine, Baylor College of Medicine, Houston, Texas, United States; Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, Texas, United States; The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, Texas, United States; Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, New York, United States; Department of Ophthalmology, University of Texas Medical Branch, Galveston, Texas, United States; University of Texas MD Anderson Cancer Center, Houston, Texas, United States; Texas A&M College of Medicine, Texas, United States; Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States.
  • Alireza Tavakkoli
    Department of Computer Science and Engineering, University of Nevada School of Medicine, Reno, NV 89557, USA.

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