Denoising diffusion-based anterior segment optical coherence tomography (AS-OCT) image generation

This study aims to address the scarcity of annotated Anterior Segment Optical Coherence Tomography (AS-OCT) datasets in ophthalmology by using Denoising Diffusion Generative Adversarial Networks (DD-GANs) to generate synthetic AS-OCT images to produce predictive models. The goal is to produce high-quality, diverse, and realistic data supporting the training of predictive models without data imbalance issues. DD-GANs effectively generate realistic AS-OCT images, producing high-quality, balanced datasets that can address data scarcity and imbalance in ophthalmology. These synthetic datasets can enhance machine learning model development, advancing medical image analysis. Synthetic medical image generation provides significant advantages in protecting personal data privacy: by using artificially generated data instead of real data, patients’ identities and confidentiality are safeguarded.

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Recommended citation: Ersarı B., Kola M. G., Karaca E. E., Işık F. D., Kemer Ö. E., Keçeli A. S., Kaya A., Gürgen Erdoğan T., Uçan A., (2025) Denoising diffusion-based anterior segment optical coherence tomography (AS-OCT) image generation, International Ophthalmology, 45, 431