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

Mouayad Masalkhi,

Kyle Sporn,

Rahul Kumar

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: May 20, 2025

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

Language: Английский

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

Mouayad Masalkhi,

Kyle Sporn,

Rahul Kumar

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: May 20, 2025

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

Language: Английский

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