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

作者：Lixue Liu, Jiaming Hong, Yuxuan Wu, Shaopeng Liu, Kai Wang, Mingyuan Li, Lanqin Zhao, Zhenzhen Liu, Longhui Li, Tingxin Cui, Ching‐Kit Tsui, Fabao Xu, Weiling Hu, Dongyuan Yun, Xi Chen, Yuanjun Shang, Shaowei Bi, Xiaoyue Wei, Yunxi Lai, Duoru Lin, Zhe Fu, Yaru Deng, Kaimin Cai, Yi Xie, Zizheng Cao, Dongni Wang, Xulin Zhang, Meimei Dongye, Haotian Lin, Xiaohang Wu · 发表于：British Journal of Ophthalmology · 年份：2024 · DOI：10.1136/bjo-2024-325403 · 被引用次数：11 · 研究领域：Retinal Imaging and Analysis、Retinal Diseases and Treatments、Ophthalmology and Visual Health Research

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 (C 2 ycleGAN) 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 evaluated the quality, authenticity and diagnostic efficacy of the generated images. Results A total of 959 CFP and 1009 UWF image pairs were included in model development. FID and KID indicated that images generated by C 2 ycleGAN presented significantly improved quality. Based on ophthalmologists’ average ratings, the percentages of inadequate-quality images decreased from 32% to 18.8% for CFP, and from 18.7% to 14.7% for UWF. Only 24.8% and 13.8% of generated CFP and UWF images could be recognised as synthetic. The accuracy of retinopathy detection significantly increased from 78% to 91% for CFP and from 91% to 93% for UWF. For retinopathy subtype diagnosis, the accuracies also increased from 87%–94% to 91%–100% fo...