ArticleInvestigative ophthalmology & visual science2023
A Fully Automatic Estimation of Tear Meniscus Height Using Artificial Intelligence.
Article in Investigative ophthalmology & visual science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Analysis of the Association Between Weight Status and Myopia in Children and Adolescents and Development of a Screening Model Based on Body Fat Percentage.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Article
- Interpretable machine learning model based on blood parameters for screening high myopia.Eye (London, England) · 2026Article
- Acute and long-term effects of ocular surface burns on ocular surface microbiota: a one-year follow-up study.Frontiers in cellular and infection microbiology · 2026Article
- Strong association between decreased stroma in Sattler's layer and high myopia.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- A deep learning-assisted automatic measurement of tear meniscus height on ocular surface images and its application in myopia control.Frontiers in bioengineering and biotechnology · 2025Article
- Advances in AI-assisted quantification of dry eye indicators.Frontiers in medicine · 2025Review
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- Feasibility of Tear Meniscus Height Measurements Obtained with a Smartphone-Attachable Portable Device and Agreement of the Results with Standard Slit Lamp Examination.Diagnostics (Basel, Switzerland) · 2024Article
- Validation of reliability, repeatability and consistency of three-dimensional choroidal vascular index.Scientific reports · 2024Article
- Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.Journal of current 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Accurate quantification measurement of tear meniscus is vital for the precise diagnosis of dry eye. In current clinical practice, the measurement of tear meniscus height (TMH) relies on doctors' manual operation. This study aims to propose a novel automatic artificial intelligence (AI) system to evaluate TMH. Methods: A total of 510 photographs obtained by the oculus camera were labeled. Three thousand and five hundred images were finally attained by data enhancement to train the neural network model parameters, and 60 were used to evaluate the model performance in segmenting the cornea and tear meniscus region. One hundred images were used to test generalization ability of the model. We modified a segmentation model of the cornea and the tear meniscus based on the UNet-like network. The output of the segmentation model is followed by a calculation module that calculates and reports the TMH. Results: Compared with ground truth (GT) manually labeled by clinicians, our modified model achieved a Dice Similarity Coefficient (DSC) and Intersection over union (Iou) of 0.99/0.98 in the corneal segmentation task and 0.92/0.86 for the detection of tear meniscus on the validation set, respectively. On the test set, the TMH automatically measured by our AI system strongly correlates with the results manually calculated by the ophthalmologists. Conclusions: We developed a fully automated and reliable AI system to obtain TMH. After large-scale clinical testing, our method could be used for dry eye screening in 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.