ArticleTranslational vision science & technology2021
Meibography Phenotyping and Classification From Unsupervised Discriminative Feature Learning.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 32 citations in OpenAlex.
- ADAM-Net: Anatomy-Guided Attentive Unsupervised Domain Adaptation for Joint MG Segmentation and MGD Grading.Journal of imaging · 2026Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- 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
- Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes.Scientific reports · 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
- 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
- Meibomian gland alterations in allergic conjunctivitis: insights from a novel quantitative analysis algorithm.Frontiers in cell and developmental biology · 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
- Development of Artefact-Free Imaging System for Accurate Meibomian Gland Reflectivity Assessment.Translational vision science & technology · 2023Article
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
- Meibomian Gland Density: An Effective Evaluation Index of Meibomian Gland Dysfunction Based on Deep Learning and Transfer Learning.Journal of clinical medicine · 2022Article
- Hypochlorous Acid Can Be the Novel Option for the Meibomian Gland Dysfunction Dry Eye through Ultrasonic Atomization.Disease markers · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The purpose of this study was to develop an unsupervised feature learning approach that automatically measures Meibomian gland (MG) atrophy severity from meibography images and discovers subtle relationships between meibography images according to visual similarity. Methods: One of the latest unsupervised learning approaches is to apply feature learning based on nonparametric instance discrimination (NPID), a convolutional neural network (CNN) backbone model trained to encode meibography images into 128-dimensional feature vectors. The network aims to learn a similarity metric across all instances (e.g. meibography images) and groups visually similar instances together. A total of 706 meibography images with corresponding meiboscores were collected and annotated for the use of network learning and performance evaluation. Results: Four hundred ninety-seven meibography images were used for network learning and tuning, whereas the remaining 209 images were used for network model evaluations. The proposed nonparametric instance discrimination approach achieved 80.9% meiboscore grading accuracy on average, outperforming the clinical team by 25.9%. Additionally, a 3D feature visualization and agglomerative hierarchical clustering algorithms were used to discover the relationship between meibography images. Conclusions: The proposed NPID approach automatically analyses MG atrophy severity from meibography images without prior image annotations, and categorizes the gland characteristics through hierarchical clustering. This method provides quantitative information on the MG atrophy severity based on the analysis of phenotypes. Translational Relevance: The study presents a Meibomian gland atrophy evaluation method for meibography images based on unsupervised learning. This method may be used to aid diagnosis and management of Meibomian gland dysfunction without prior image annotations, which require time and resources.
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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.