| Title: | DiFFA: A Diffusion-Based Model for Synthetic Fundus Fluorescein Angiography to Enhance Detection of Diabetic Microvascular Complication. |
| Journal: | Translational Vision Science & Technology |
| Published: | 18 Feb 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41705771/ |
| DOI: | https://doi.org/10.1167/tvst.15.2.21 |
| URL: | http://dx.doi.org/10.1167/tvst.15.2.21 |
Publication 17707
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Abstract
Purpose: To develop diffusion-based fundus fluorescein angiography (DiFFA), a noninvasive method for generating high-quality FFA images from color fundus (CF) photographs using diffusion models, addressing the limitations of invasive FFA examinations in detecting diabetic microvascular complications.</p>
Methods: DiFFA was trained on paired CF and FFA images from 4032 patients across 3 hospitals, with external validation using 2000 CF images from the UK Biobank. Four systematic evaluations were conducted: (1) objective image quality assessment using the mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and Fréchet inception distance metrics, (2) subjective authenticity evaluation by ophthalmologists, (3) diagnostic efficiency assessment, and (4) automated classification accuracy for diabetic retinopathy and diabetic kidney disease using VGG16 with and without synthetic FFA images.</p>
Results: DiFFA achieved strong objective performance (mean absolute error, 109.25,; peak signal-to-noise ratio, 27.677; Fréchet inception distance, 38.466; structural similarity index measure, 0.694). Ophthalmologists showed limited ability to distinguish synthetic from real FFA images (area under the curve, 0.52 ± 0.06 for junior ophthalmologists and 0.57 ± 0.05 for senior ophthalmologists), with no significant quality score differences (P > 0.05). Combining synthetic FFA with CF images improved automated classification, achieving an area under the curve of 0.933 for diabetic retinopathy and 0.849 for diabetic kidney disease internally, compared with 0.891 and 0.792 using CF photographs alone. External validation confirmed improvements (diabetic retinopathy, 0.858 to 0.879; diabetic kidney disease, 0.731 to 0.814).</p>
Conclusions: DiFFA successfully generates clinically authentic FFA images from non-invasive CF photographs, enhancing diagnostic accuracy while overcoming the invasiveness limitations of traditional FFA examinations.</p>
Translational Relevance: Diffusion-based fundus fluorescein angiography enables noninvasive early detection of diabetic microvascular complications, improving patient compliance and diagnostic accessibility while reducing examination risks in diabetes screening and monitoring.</p>
8 Keywords
- Diabetic Nephropathies
- Diabetic Retinopathy
- Female
- Fluorescein Angiography
- Fundus Oculi
- Humans
- Male
- Middle Aged
17 Authors
- Jin Wei
- Yupeng Xu
- Tian Niu
- Li Su
- Yinchen Shen
- Xindan Xing
- Yan Jiang
- Xiaoxin Liu
- Qian Zhu
- Jingtong Xie
- Xinyi Liu
- Kangjia Lv
- Lei Bi
- Yige Peng
- Xun Xu
- Chong Chen
- Kun Liu
Enabling scientific discoveries that improve human health