Abstract
Fluorescein angiography is a crucial examination in ophthalmology to identify retinal and choroidal pathologies. However, this examination modality is invasive and inconvenient, requiring intravenous injection of a fluorescent dye. In order to provide a more convenient option for high-risk patients, we propose a deep-learning-based method to translate fundus photography into fluorescein angiography using Energy-based Cycle-consistent Adversarial Networks (CycleEBGAN) We propose a deep-learning-based method to translate fundus photography into fluorescein angiography using CycleEBGAN. We collected fundus photographs and fluorescein angiographs taken at Changwon Gyeongsang National University Hospital between January 2016 and June 2021 and paired late-phase fluorescein angiographs and fundus photographs taken on the same day. We developed CycleEBGAN, a combination of cycle-consistent adversarial networks (CycleGAN) and Energy-based Generative Adversarial Networks (EBGAN), to translate the paired images. The simulated images were then interpreted by 2 retinal specialists to determine their clinical consistency with fluorescein angiography. A retrospective study. A total of 2605 image pairs were obtained, with 2555 used as the training set and the remaining 50 used as the test set. Both CycleGAN and CycleEBGAN effectively translated fundus photographs into fluorescein angiographs. However, CycleEBGAN showed superior results to CycleGAN in translating subtle abnormal features. We propose CycleEBGAN as a method for generating fluorescein angiography using cheap and convenient fundus photography. Synthetic fluorescein angiography with CycleEBGAN was more accurate than fundus photography, making it a helpful option for high-risk patients requiring fluorescein angiography, such as diabetic retinopathy patients with nephropathy.