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Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence

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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.

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Authors and Affiliations

  1. University College Dublin School of Medicine, Dublin, Belfield, Ireland

    Mouayad Masalkhi

  2. Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland

    Mouayad Masalkhi

  3. Department of Medicine, SUNY Upstate Medical University Norton College of Medicine, Syracuse, NY, 13210, USA

    Kyle Sporn

  4. Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL, 13210, USA

    Rahul Kumar

  5. Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, MI, 13210, USA

    Joshua Ong

  6. Weill Cornell/Rockefeller/Sloan-Kettering Tri-Institutional MD-PhD Program, New York, NY, 13210, USA

    Tuan Nguyen

  7. Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK

    Ethan Waisberg

  8. Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, NV, 13210, USA

    Nasif Zaman & Alireza Tavakkoli

  9. Center for Space Medicine, Baylor College of Medicine, Houston, TX, 13210, USA

    Andrew G. Lee

  10. Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, 13210, USA

    Andrew G. Lee

  11. The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, 13210, USA

    Andrew G. Lee

  12. Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, 13210, USA

    Andrew G. Lee

  13. Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, 13210, USA

    Andrew G. Lee

  14. University of Texas MD Anderson Cancer Center, Houston, TX, 13210, USA

    Andrew G. Lee

  15. Texas A&M College of Medicine, Texas, TX, 13210, USA

    Andrew G. Lee

  16. Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, 13210, USA

    Andrew G. Lee

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Contributions

M.M. contributed to the conceptualization of the study, writing of the manuscript, and revising the material. K.S. contributed to content analysis, writing portions of the manuscript, and revising the manuscript and tables and information. R.K. contributed to content analysis, writing portions of the manuscript, and revising the tables and information. J.O. contributed to the conceptualization of the study, writing of the manuscript, and reviewing the content. T.N. contributed to the conceptualization of the study, writing of the manuscript, and reviewing the content. E.W. contributed to the conceptualization of the study, writing of the manuscript, and reviewing the content. N.Z. contributed to the literature review, manuscript drafting, and analysis of related studies in ophthalmic imaging. A.G.L. provided intellectual support, supervision, and review of the content. A.T. provided intellectual support, supervision, and review of the content.

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Correspondence to Mouayad Masalkhi.

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Masalkhi, M., Sporn, K., Kumar, R. et al. Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence. J Digit Imaging. Inform. med. (2025). https://doi.org/10.1007/s10278-025-01519-1

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