Trial reportBMC ophthalmology2022
Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.
Trial report in BMC ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05105620 (Bridging the Resources Gap), which is not on this map. Cited by 5 papers.
What it found
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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.
Bridging the Resources Gap: Deep Learning for Fluorescein Angiography and Optical Coherence Tomography Macular Thickness Map Image Translation
Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Deep Learning Prediction of Retinal Thickness from Near-Infrared Fundus Photography: Toward Decentralized Quantitative Assessment of Diabetic Macular Edema.Journal of personalized medicine · 2026Article
- A Future Picture: A Review of Current Generative Adversarial Neural Networks in Vitreoretinal Pathologies and Their Future Potentials.Biomedicines · 2025Review
- The quest for early detection of retinal disease: 3D CycleGAN-based translation of optical coherence tomography into confocal microscopy.Biological imaging · 2024Article
- Discriminative, generative artificial intelligence, and foundation models in retina imaging.Taiwan journal of ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo assess the ability of the pix2pix generative adversarial network (pix2pix GAN) to synthesize clinically useful optical coherence tomography (OCT) color-coded macular thickness maps based on a modest-sized original fluorescein angiography (FA) dataset and the reverse, to be used as a plausible alternative to either imaging technique in patients with diabetic macular edema (DME).
methodsOriginal images of 1,195 eyes of 708 nonconsecutive diabetic patients with or without DME were retrospectively analyzed. OCT macular thickness maps and corresponding FA images were preprocessed for use in training and testing the proposed pix2pix GAN. The best quality synthesized images using the test set were selected based on the Fréchet inception distance score, and their quality was studied subjectively by image readers and objectively by calculating the peak signal-to-noise ratio, structural similarity index, and Hamming distance. We also used original and synthesized images in a trained deep convolutional neural network (DCNN) to plot the difference between synthesized images and their ground-truth analogues and calculate the learned perceptual image patch similarity metric.
resultsThe pix2pix GAN-synthesized images showed plausible subjectively and objectively assessed quality, which can provide a clinically useful alternative to either image modality.
conclusionUsing the pix2pix GAN to synthesize mutually dependent OCT color-coded macular thickness maps or FA images can overcome issues related to machine unavailability or clinical situations that preclude the performance of either imaging technique.
trial registrationClinicalTrials.gov Identifier: NCT05105620, November 2021. "Retrospectively registered".
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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 OpenQuestion graph.