ArticleOptometry and vision science : official publication of the American Academy of Optometry2021
Quantifying Meibomian Gland Morphology Using Artificial Intelligence.
Article in Optometry and vision science : official publication of the American Academy of Optometry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Advances in Dry Eye Disease Examination Techniques.Frontiers in medicine · 2021Pooled it
- WRCANet: wavelet residual cross-attention network for meibomian gland segmentation.Biomedical optics express · 2026Article
- AI driven quantitative analysis of meibomian glands in children and adolescents: a benchmark dataset study.Eye and vision (London, England) · 2025Article
- Impact of definition of meibomian gland dysfunction on the frequency of MGD in dry eye disease from the DREAM© study.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2025Article
- Ophthalmic Segmentation and Analysis Software (OASIS): A Comprehensive Tool for Quantitative Evaluation of Meibography Images.Translational vision science & technology · 2025Article
- Morphological Characteristics of the Meibomian Glands: Impact of Demographics and Contact Lens Wear.Translational vision science & technology · 2025Article
- Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation.NPJ digital medicine · 2025Article
- A modular and adaptable approach for automated morphological feature extraction in meibography images.Scientific reports · 2025Article
- Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes.Scientific reports · 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
- Ocular surface disease following LASIK and cataract surgery: a review of their interrelated complications.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
- Insight: A Multi-Modal Diagnostic Pipeline using LLMs for Ocular Surface Disease Diagnosis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- A machine learning approach to predicting dry eye-related signs, symptoms and diagnoses from meibography images.Heliyon · 2024Article
- Enhancing Meibography Image Analysis Through Artificial Intelligence-Driven Quantification and Standardization for Dry Eye Research.Translational vision science & technology · 2024Article
- A Deep Learning Approach for Meibomian Gland Appearance Evaluation.Ophthalmology science · 2023Article
- Unsupervised Learning Based on Meibography Enables Subtyping of Dry Eye Disease and Reveals Ocular Surface Features.Investigative ophthalmology & visual science · 2023Article
- Meibomian gland stem/progenitor cells: The hunt for gland renewal.The ocular surface · 2023Article
- Intense Pulsed Light Therapy in the Treatment of Dry Eye Diseases: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
significanceQuantifying meibomian gland morphology from meibography images is used for the diagnosis, treatment, and management of meibomian gland dysfunction in clinics. A novel and automated method is described for quantifying meibomian gland morphology from meibography images. PURPOSE: Meibomian gland morphological abnormality is a common clinical sign of meibomian gland dysfunction, yet there exist no automated methods that provide standard quantifications of morphological features for individual glands. This study introduces an automated artificial intelligence approach to segmenting individual meibomian gland regions in infrared meibography images and analyzing their morphological features.
methodsA total of 1443 meibography images were collected and annotated. The dataset was then divided into development and evaluation sets. The development set was used to train and tune deep learning models for segmenting glands and identifying ghost glands from images, whereas the evaluation set was used to evaluate the performance of the model. The gland segmentations were further used to analyze individual gland features, including gland local contrast, length, width, and tortuosity.
resultsA total of 1039 meibography images (including 486 upper and 553 lower eyelids) were used for training and tuning the deep learning model, whereas the remaining 404 images (including 203 upper and 201 lower eyelids) were used for evaluations. The algorithm on average achieved 63% mean intersection over union in segmenting glands, and 84.4% sensitivity and 71.7% specificity in identifying ghost glands. Morphological features of each gland were also fed to a support vector machine for analyzing their associations with ghost glands. Analysis of model coefficients indicated that low gland local contrast was the primary indicator for ghost glands.
conclusionsThe proposed approach can automatically segment individual meibomian glands in infrared meibography images, identify ghost glands, and quantitatively analyze gland morphological features.
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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.