Deep learning-enabled transformation of anterior segment images to corneal fluorescein staining images for enhanced corneal disease screening

Abstract

Corneal diseases present a significant challenge to global health. Given the uneven distribution of ophthalmic resources, the development of a system to facilitate remote diagnosis of corneal diseases is particularly crucial. In this study, we developed an artificial intelligence system named Gancor, based on a large-scale clinical dataset comprising 9669 anterior segment (AS) images and corresponding corneal fluorescein staining (CFS) images from the Affiliated Eye Hospital of Nanchang University, as well as 967 pairs of AS-CFS images captured via smartphone from the Jiangxi Province Division of National Clinical Research Center for Ocular Diseases. The system utilizes Generative Adversarial Networks (GANs) to convert AS images into CFS images for the screening of 11 common corneal diseases. Objective assessments of the generated CFS images were conducted using Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), along with subjective evaluations by three experienced ophthalmologists, confirming the high quality and diagnostic relevance of the synthesized images. In terms of diagnostic performance for corneal diseases, the accuracy rate exceeded 75 %, and the Area Under the Curve (AUC) value reached above 0.90. This innovative approach not only provides images with greater diagnostic value for telemedicine but also enhances the efficiency of remote diagnosis, offering an effective tool for achieving the goal of comprehensive, equitable, and accessible eye care services.

Keywords

• Deep learning
• Generative Adversarial Networks
• Corneal fluorescein staining
• Anterior segment