ArticleInternational journal of ophthalmology2026
Artificial intelligence in ophthalmic optical coherence tomography angiography: a bibliometric analysis.
Article in International journal of ophthalmology, 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo analyze research landscapes, hotspots, and trends in artificial intelligence (AI) applied to ophthalmic optical coherence tomography angiography (OCTA) from 2015 to 2024, aiming to reveal global collaborations and guide future research.
methodsWe retrieved publications from Web of Science Core Collection using AI and OCTA-related search terms. After screening, 237 articles were analyzed with CiteSpace (version 6.3.R1) for co-citation, collaboration, and keyword burst analysis.
resultsAnnual publications grew rapidly since 2017. China (94 publications) and the US (86 publications) were the most productive and influential countries. Research focused on: deep learning algorithms and vessel segmentation, ocular disease diagnosis including diabetic retinopathy and glaucoma, and automated vascular quantification and multimodal imaging. Key limitations included small sample sizes and limited algorithm generalizability.
conclusionThe integration of AI with OCTA has significantly advanced the automation and precise quantitative analysis of ophthalmic vascular imaging, demonstrating substantial potential for diagnosing various retinal and choroidal diseases. Future efforts should focus on developing more robust and interpretable AI models, conducting large-scale, multi-center clinical validation, and establishing standardized imaging and analysis protocols to ultimately realize the widespread adoption of AI-OCTA technology in routine clinical practice.
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.