ArticleJournal of clinical medicine2023
A Deep Learning Model for Evaluating Meibomian Glands Morphology from Meibography.
Article in Journal of clinical medicine, 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
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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
10 citing papers in PubMed.
- Application of a deep learning-based system for eyelid margin signs identification training and testing in dry eye disease education.Frontiers in medicine · 2026Article
- A Comprehensive Review Comparing Artificial Intelligence and Clinical Diagnostic Approaches for Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2025Review
- AI driven quantitative analysis of meibomian glands in children and adolescents: a benchmark dataset study.Eye and vision (London, England) · 2025Article
- A modular and adaptable approach for automated morphological feature extraction in meibography images.Scientific reports · 2025Article
- Advancing Meibography Assessment and Automated Meibomian Gland Detection Using Gray Value Profiles.Diagnostics (Basel, Switzerland) · 2025Article
- Internal validation of a convolutional neural network pipeline for assessing meibomian gland structure from meibography.Optometry and vision science : official publication of the American Academy of Optometry · 2025Article
- Advances in AI-assisted quantification of dry eye indicators.Frontiers in medicine · 2025Review
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease.Scientific reports · 2024Article
- Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.Journal of current ophthalmologyReview
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
To develop a deep learning model for automatically segmenting tarsus and meibomian gland areas on meibography, we included 1087 meibography images from dry eye patients. The contour of the tarsus and each meibomian gland was labeled manually by human experts. The dataset was divided into training, validation, and test sets. We built a convolutional neural network-based U-net and trained the model to segment the tarsus and meibomian gland area. Accuracy, sensitivity, specificity, and receiver operating characteristic curve (ROC) were calculated to evaluate the model. The area under the curve (AUC) values for models segmenting the tarsus and meibomian gland area were 0.985 and 0.938, respectively. The deep learning model achieved a sensitivity and specificity of 0.975 and 0.99, respectively, with an accuracy of 0.985 for segmenting the tarsus area. For meibomian gland area segmentation, the model obtained a high specificity of 0.96, with high accuracy of 0.937 and a moderate sensitivity of 0.751. The present research trained a deep learning model to automatically segment tarsus and the meibomian gland area from infrared meibography, and the model demonstrated outstanding accuracy in segmentation. With further improvement, the model could potentially be applied to assess the meibomian gland that facilitates dry eye evaluation in various clinical and research scenarios.
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Registered trials
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