ArticleOphthalmology and therapy2022
Generating Synthesized Ultrasound Biomicroscopy Images from Anterior Segment Optical Coherent Tomography Images by Generative Adversarial Networks for Iridociliary Assessment.
Article in Ophthalmology and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.Journal of imaging informatics in medicine · 2026Pooled it
- Development trajectory and trends of ultrasound biomicroscopy in glaucoma research: a comprehensive 20-year bibliometric analysis.International ophthalmology · 2026Review
- Deep learning-based anterior segment identification and parameter assessment of primary angle closure disease in ultrasound biomicroscopy images.BMJ open ophthalmology · 2025Article
- Generating Synthesized Fluorescein Angiography Images From Color Fundus Images by Generative Adversarial Networks for Macular Edema Assessment.Translational vision science & technology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
introductionThe aim of this study was to investigate the feasibility of generating synthesized ultrasound biomicroscopy (UBM) images from swept-source anterior segment optical coherent tomography (SS-ASOCT) images using a cycle-consistent generative adversarial network framework (CycleGAN) for iridociliary assessment on a cohort presenting for primary angle-closure screening.
methodsThe CycleGAN architecture was adopted to synthesize high-resolution UBM images trained on the SS-ASOCT dataset from the department of ophthalmology, Xinhua Hospital. The performance of the CycleGAN model was further tested in two separate datasets using synthetic UBM images from two different ASOCT modalities (in-distribution and out-of-distribution). We compared the ability of glaucoma specialists to assess the image quality of real and synthetic images. UBM measurements, including anterior chamber, iridociliary parameters, were compared between real and synthetic UBM images. Intra-class correlation coefficients, coefficients of variation, and Bland-Altman plots were used to assess the level of agreement. The Fréchet Inception Distance (FID) was measured to evaluate the quality of the synthetic images.
resultsThe whole trained dataset included anterior chamber angle images, of which 4037 were obtained by SS-ASOCT and 2206 were obtained by UBM. The image quality of real versus synthetic SS-ASOCT images was similar as assessed by two glaucoma specialists. The Bland-Altman analysis also suggested high consistency between measurements of real and synthetic UBM images. In addition, there was fair to excellent agreement between real and synthetic UBM measurements for the in-distribution dataset (ICC range 0.48-0.97) and the out-of-distribution dataset (ICC range 0.52-0.86). The FID was 21.3 and 24.1 for the synthetic UBM images from the in-distribution and out-of-distribution datasets, respectively.
conclusionWe developed a CycleGAN model to translate UBM images from non-contact SS-ASOCT images. The CycleGAN synthetic UBM images showed fair to excellent reproducibility when compared with real UBM images. Our results suggest that the CycleGAN technique is a promising tool to evaluate the iridociliary and anterior chamber in an alternative non-contact method.
Indexed as
Identifiers
What 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.