ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
Diagnosability of Synthetic Retinal Fundus Images for Plus Disease Detection in Retinopathy of Prematurity.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed, 7 citations in OpenAlex.
- Augmenting a prognostic deep learning system for referable diabetic retinopathy and maculopathy with synthetic retinal images.Communications medicine · 2025Article
- Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2022Article
- Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence: Application to Retinopathy of Prematurity Diagnosis.Ophthalmology science · 2022Article
- Deepfakes in Ophthalmology: Applications and Realism of Synthetic Retinal Images from Generative Adversarial Networks.Ophthalmology science · 2021Article
- A GAN-based deep enhancer for quality enhancement of retinal images photographed by a handheld fundus camera.Advances in ophthalmology practice and researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Advances in generative adversarial networks have allowed for engineering of highly-realistic images. Many studies have applied these techniques to medical images. However, evaluation of generated medical images often relies upon image quality and reconstruction metrics, and subjective evaluation by laypersons. This is acceptable for generation of images depicting everyday objects, but not for medical images, where there may be subtle features experts rely upon for diagnosis. We implemented the pix2pix generative adversarial network for retinal fundus image generation, and evaluated the ability of experts to identify generated images as such and to form accurate diagnoses of plus disease in retinopathy of prematurity. We found that, while experts could discern between real and generated images, the diagnoses between image sets were similar. By directly evaluating and confirming physicians' abilities to diagnose generated retinal fundus images, this work supports conclusions that generated images may be viable for dataset augmentation and physician training.
Indexed as
Identifiers
33936405PMC8075515W3157073783What OpenQuestion 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 OpenQuestion graph.