Observational studyTranslational vision science & technology2026
DiFFA: A Diffusion-Based Model for Synthetic Fundus Fluorescein Angiography to Enhance Detection of Diabetic Microvascular Complication.
Observational study in Translational vision science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
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. 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. 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). 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. 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.