Digital ray: enhancing cataractous fundus images using style transfer generative adversarial networks to improve retinopathy detection.

ABSTRACT:

Background/aims

The aim of this study was to develop and evaluate digital ray, based on preoperative and postoperative image pairs using style transfer generative adversarial networks (GANs), to enhance cataractous fundus images for improved retinopathy detection.

Methods

For eligible cataract patients, preoperative and postoperative colour fundus photographs (CFP) and ultra-wide field (UWF) images were captured. Then, both the original CycleGAN and a modified CycleGAN (C²ycleGAN) framework were adopted for image generation and quantitatively compared using Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Additionally, CFP and UWF images from another cataract cohort were used to test model performances. Different panels of ophthalmologists eva

SUBMITTER: Liu L 

PROVIDER: S-EPMC11503040 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature