ArticleOptics express2025
Artificial intelligence-assisted projection-resolved optical coherence tomographic angiography (aiPR-OCTA).
Article in Optics express, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Widefield OCT angiography.Progress in retinal and eye research · 2025Review
- Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.Frontiers in ophthalmology · 2023Article
- Advancing optical coherence tomography angiography to the clinic.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.
Funding
Abstract
We improved voxel-wise projection-resolved optical coherence tomographic angiography (PR-OCTA) using artificial intelligence. For generating a high-quality ground truth, our approach involved graders editing the flow signal to achieve an optimal appearance in the inner/outer retina and choroid through a rule-based PR-OCTA algorithm, ensuring the preservation of in situ flow signals (ground truth) while removing residual artifacts. The developed model employs a convolutional neural network to generate projection-resolved OCTA volumes from structural OCT and OCTA inputs. We evaluated the artificial intelligence PR-OCTA (aiPR-OCTA) algorithm on 126 normal eyes by assessing structural similarity (SSIM), flow signal-to-noise ratio (fSNR), and residual artifact strength. Compared to the existing state-of-the-art rule-based PR-OCTA algorithm, aiPR-OCTA demonstrated superior artifact removal, better preservation of flow signals, and accurate maintenance of anatomical details at the capillary scale. Additionally, it achieved a higher fSNR and reduced background artifacts.
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.