UWF-RI2FA: Generating Multi-frame Ultrawide-field Fluorescein Angiography from Ultrawide-field Retinal Imaging Improves Diabetic Retinopathy Stratification
This addresses the need for safer, noninvasive diabetic retinopathy screening by eliminating intravenous dye injections, though it is incremental as it applies existing generative AI methods to a medical imaging task.
The study tackled the problem of generating dye-free ultrawide-field fluorescein angiography (UWF-FA) images from noninvasive retinal imaging using a GAN-based model, resulting in high authenticity scores (e.g., multi-scale similarity 0.70-0.91) and significantly improving diabetic retinopathy classification AUROC from 0.869 to 0.904.
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 retinal imaging (UWF-RI) 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-RI 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-RI images (P < .001). The model successfully generates realistic multi-frame UWF-FA images for enhancing DR stratification without intravenous dye injection.