Ultrawide-field fluorescein angiography (UWF-FA) facilitates diabetic retinopathy (DR) detection by providing a clear visualization of peripheral retinal lesions. However, the intravenous dye injection with potential risks hamper its application. We aim to acquire dye-free UWF-FA images from noninvasive UWF color fundus (UWF-CF) images using generative artificial intelligence (GenAI) and evaluate its effectiveness in DR screening. A total of 18,321 UWF-FA images of different phases were registered with corresponding UWF-CF images and fed into a generative adversarial networks (GAN)-based model for training. The quality of generated UWF-FA images was evaluated through quantitative metrics and human evaluation. The DeepDRiD dataset was used to externally assess the contribution of generated UWF-FA images to DR classification, using area under the receiver operating characteristic curve (AUROC) as outcome metrics. The generated early, mid, and late phase UWF-FA images achieved high authenticity, with multi-scale similarity scores ranging from 0.70 to 0.91 and qualitative visual scores ranging from 1.64 to 1.98 (1=real UWF-FA quality). In fifty randomly selected images, 56% to 76% of the generated images were difficult to distinguish from real images in the Turing test. Moreover, adding these generated UWF-FA images for DR classification significantly increased the AUROC from 0.869 to 0.904 compared to the baseline model using UWF-CF images (P < .001). The model successfully generates realistic multi-frame UWF-FA images without intravenous dye injection. The generated UWF-FA enhanced DR stratification.

翻译：超广角荧光素血管造影（UWF-FA）通过清晰显示周边视网膜病变，有助于糖尿病视网膜病变（DR）的检测。然而，静脉注射染料的潜在风险限制了其应用。本研究旨在利用生成式人工智能（GenAI）从无创的超广角彩色眼底（UWF-CF）图像生成无需染料的UWF-FA图像，并评估其在DR筛查中的有效性。研究将18,321张不同时相的UWF-FA图像与对应的UWF-CF图像配准后，输入基于生成对抗网络（GAN）的模型进行训练。通过定量指标和人工评估对生成的UWF-FA图像质量进行评价。利用DeepDRiD数据集外部评估生成的UWF-FA图像对DR分类的贡献，以受试者工作特征曲线下面积（AUROC）作为结果指标。生成的早期、中期及晚期UWF-FA图像均具有较高的真实性，多尺度相似度得分范围为0.70至0.91，定性视觉评分范围为1.64至1.98（1分表示达到真实UWF-FA质量）。在随机选取的50张图像中，56%至76%的生成图像在图灵测试中难以与真实图像区分。此外，与仅使用UWF-CF图像的基线模型相比，加入这些生成的UWF-FA图像进行DR分类，使AUROC从0.869显著提升至0.904（P < .001）。该模型成功实现了无需静脉注射染料即可生成逼真的多帧UWF-FA图像。生成的UWF-FA图像有效增强了DR分层能力。