ReviewPLOS digital health2026
Recent advances in corneal specular microscopy image analysis through artificial intelligence.
Review in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Repeatability and reproducibility of corneal endothelial measurements using the topcon SP-1P specular microscope.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although conventional automated analysis of corneal specular microscopy images has historically been limited by reproducibility challenges in the presence of corneal guttae, recent advances in artificial intelligence (AI) have significantly enhanced its diagnostic potential in such cases. This review explores the integration of AI techniques for analyzing specular microscopy images, emphasizing the shift from classical to advanced AI methods. We highlight AI-based methodologies-supervised and unsupervised learning-that have significantly enhanced the accuracy of in vivo human corneal endothelium analysis. The paper also discusses the challenges in data collection, emphasizing ethical considerations and the need for high-quality datasets. Additionally, we explore novel AI-derived metrics and their implications in enhancing diagnostic precision, particularly in Fuchs endothelial corneal dystrophy. The review concludes with insights into the future direction of AI in specular microscopy, highlighting its increasing relevance in ocular healthcare and the potential to overcome longstanding limitations in the field.
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.