SynthesisFrontiers in research metrics and analytics2026
Healthcare analytics perspectives on artificial intelligence-based retinopathy of prematurity screening: a bibliometric mapping study.
Synthesis in Frontiers in research metrics and analytics, 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
The integration of artificial intelligence (AI) into retinopathy of prematurity (ROP) screening represents an important advancement in neonatal ophthalmology, particularly in settings facing specialist shortages and screening infrastructure constraints. Despite the rapid expansion of research in this domain, a structured evaluation of its intellectual landscape, geographic distribution, and collaboration patterns remains limited. This study presents a bibliometric mapping analysis of global research on AI-based ROP screening. A total of 55 publications indexed in the Scopus database were identified through a reproducible search strategy and analyzed using Bibliometrix and VOSviewer. The retrieved publications span the period 2017-2025. The results demonstrate a marked upward trajectory in publications, with a peak of 16 publications in 2025 and an estimated annual growth rate of 41.42%. The analysis examines publication trends, geographic productivity, institutional collaboration networks, citation patterns, and thematic evolution. The findings indicate strong contributions from Asia and increasing participation from the Middle East, particularly Türkiye, Saudi Arabia, and Egypt. International co-authorship networks reveal structured South-North collaborations linking Middle Eastern countries with major research hubs in the United States and the United Kingdom. Citation analysis shows an average of 18.27 citations per publication. Thematic mapping highlights the dominance of deep learning and retinal image analysis, alongside emerging discussions on healthcare accessibility and deployment in resource-limited settings. These results provide a structured quantitative overview of the development of AI-based ROP screening research and may support future research prioritization and strategic planning in AI-assisted neonatal eye care.
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.