ArticleInternational ophthalmology2022
Automatic identification of meibomian gland dysfunction with meibography images using deep learning.
Article in International ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 27 citations in OpenAlex.
- Bridging Therapeutics and Nanotechnology in Dry Eye Disease: A Review of Risk Factors, Drug Delivery Barriers, and Recent and Future Endeavors for Disease Management.AAPS PharmSciTech · 2026Review
- Relationship between morphological changes in meibomian glands, age, and ocular surface parameters.BMC ophthalmology · 2026Article
- AI-Assisted detection of corneal nerve structural abnormalities in early diabetic keratopathy: development and validation of a deep learning framework.Scientific reports · 2026Article
- A Comprehensive Review Comparing Artificial Intelligence and Clinical Diagnostic Approaches for Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation.NPJ digital medicine · 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
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- A machine learning approach to predicting dry eye-related signs, symptoms and diagnoses from meibography images.Heliyon · 2024Article
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- A Deep Learning Approach for Meibomian Gland Appearance Evaluation.Ophthalmology science · 2023Article
- A Deep Learning Model for Evaluating Meibomian Glands Morphology from Meibography.Journal of clinical medicine · 2023Article
- Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2022Review
- 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
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundArtificial intelligence is developing rapidly, bringing increasing numbers of intelligent products into daily life. However, it has little progress in dry eye, which is a common disease and associated with meibomian gland dysfunction (MGD). Noninvasive infrared meibography, known as an effective diagnostic tool of MGD, allows for objective observation of meibomian glands. Thus, we discuss a deep learning method to measure and assess meibomian glands of meibography.
methodsWe used Mask R-CNN deep learning (DL) framework. A total of 1878 meibography images were collected and manually annotated by two licensed eyelid specialists with two classes: conjunctiva and meibomian glands. The annotated pictures were used to establish a DL model. An independent test dataset that contained 58 images was used to compare the accuracy and efficiency of the deep learning model with specialists.
resultsThe DL model calculated the ratio of meibomian gland loss with precise values by achieving high accuracy in the identification of conjunctiva (validation loss < 0.35, mAP > 0.976) and meibomian glands (validation loss < 1.0, mAP > 0.92). The comparison between specialists' annotation and the DL model evaluation showed that there is little difference between the gold standard and the model. Each image takes 480 ms for the model to evaluate, almost 21 times faster than specialists.
conclusionsThe DL model can improve the accuracy of meibography image evaluation, help specialists to grade the meibomian glands and save their time to some extent.
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.