ArticleNPJ digital medicine2025
Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Ocular surface and meibomian gland changes following repeated exposure to 5% povidone-iodine during intravitreal injections.Eye (London, England) · 2026Article
- Application of large language models in clinical decision-making for dry eye disease.BMC medical informatics and decision making · 2026Article
- Molecular landscape connecting complications of dry eye disease: mechanisms, therapeutic targets, biomarkers, and future innovations.International ophthalmology · 2026Review
- Assessing the reliability of non-cycloplegic refraction in children: a machine learning approach based on non-cycloplegic parameters.Frontiers in public health · 2026Article
- AI driven quantitative analysis of meibomian glands in children and adolescents: a benchmark dataset study.Eye and vision (London, England) · 2025Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
This multicenter retrospective study developed and validated an AI driven model for automated segmentation and quantitative evaluation of meibomian glands using infrared meibography images acquired by the Keratograph 5M device. A total of 1350 infrared meibography images were collected and annotated for model training and validation. The model demonstrated high segmentation performance, with an Intersection over Union of 81.67% (95% Confidence Interval [CI]: 81.03-82.31) and accuracy of 97.49% (95% CI: 97.38-97.62), outperforming conventional algorithms. The agreement was observed between AI-based and manual gland grading (Kappa = 0.93) and gland counting (Spearman r = 0.9334). Repeatability analysis confirmed the model's stability, and external validation across four independent centers yielded consistent results with AUCs exceeding 0.99. This AI tool offers a standardized, efficient, and objective method for meibography image analysis, which may improve diagnostic precision and assist in the clinical management of meibomian gland dysfunction across diverse populations.
Identifiers
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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.