Applications of generative adversarial networks in the diagnosis, prognosis, and treatment of ophthalmic diseases
Year: 2025
Type: article
Abstract: PURPOSE: Generative adversarial networks (GANs) are key components of many artificial intelligence (AI) systems that are applied to image-informed bioengineering and medicine. GANs combat key limitations facing deep learning models: small, unbalanced datasets containing few images of severe disease. The predictive capacity of conditional GANs may also be extremely useful in managing disease on an individual basis. This narrative review focusses on the application of GANs in ophthalmology, in order to provide a critical account of the current state and ongoing challenges for healthcare professionals and allied scientists who are interested in this rapidly evolving field. METHODS: We performed a search of studies that apply generative adversarial networks (GANs) in diagnosis, therapy and prognosis of eight eye diseases. These disparate tasks were selected to highlight developments in GAN techniques, differences and common features to aid practitioners and future adopters in the field of ophthalmology. RESULTS: The studies we identified show that GANs have demonstrated capacity to: generate realistic and useful synthetic images, convert image modality, improve image quality, enhance extraction of relevant features, and provide prognostic predictions based on input images and other relevant data. CONCLUSION: The broad range of architectures considered describe how GAN technology is evolving to meet different challenges (including segmentation and multi-modal imaging) that are of particular relevance to ophthalmology. The wide availability of datasets now facilitates the entry of new researchers to the field. However mainstream adoption of GAN technology for clinical use remains contingent on larger public datasets for widespread validation and necessary regulatory oversight.
Institutions: University of Cambridge, University of Michigan, University of Nevada, Reno, Houston Methodist, The University of Texas MD Anderson Cancer Center, Baylor College of Medicine, University of Iowa Hospitals and Clinics, Weill Cornell Medicine, The University of Texas Medical Branch at Galveston, Texas A&M University
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,990
Award: G8847159831
OpenAlex ID: W4409656175