ArticleTranslational vision science & technology2019
A Novel Automated Approach for Infrared-Based Assessment of Meibomian Gland Morphology.
Article in Translational vision science & technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 44 citations in OpenAlex.
- ADAM-Net: Anatomy-Guided Attentive Unsupervised Domain Adaptation for Joint MG Segmentation and MGD Grading.Journal of imaging · 2026Article
- AI driven quantitative analysis of meibomian glands in children and adolescents: a benchmark dataset study.Eye and vision (London, England) · 2025Article
- Keratoconjunctivitis Sicca in Sjögren Disease: Diagnostic Challenges and Therapeutic Advances.International journal of molecular sciences · 2025Review
- Ophthalmic Segmentation and Analysis Software (OASIS): A Comprehensive Tool for Quantitative Evaluation of Meibography Images.Translational vision science & technology · 2025Article
- A modular and adaptable approach for automated morphological feature extraction in meibography images.Scientific reports · 2025Article
- Quantitative evaluation of meibomian gland dysfunction via deep learning-based infrared image segmentation.Frontiers in artificial intelligence · 2025Article
- Comparative Performance of Artificial Tear Drops Assessed with Objective Non-Invasive Imaging.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Global research of artificial intelligence in eyelid diseases: A bibliometric analysis.Heliyon · 2024Article
- Artificial intelligence in the anterior segment of eye diseases.International journal of ophthalmology · 2024Review
- Meibomian gland dropout of upper eyelids as a novel biomarker for early diagnosis of primary Sjögren's syndrome: a pilot study.Therapeutic advances in musculoskeletal disease · 2024Article
- A Deep Learning Approach for Meibomian Gland Appearance Evaluation.Ophthalmology science · 2023Article
- Automatic segmentation and quantified analysis of meibomian glands from infrared images.Indian journal of ophthalmology · 2023Observational
- The efficacy of ocular surface assessment approaches in evaluating dry eye treatment with artificial tears.Scientific reports · 2022Article
- Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2022Review
- Meibomian Gland Density: An Effective Evaluation Index of Meibomian Gland Dysfunction Based on Deep Learning and Transfer Learning.Journal of clinical medicine · 2022Article
- Is histogram manipulation always beneficial when trying to improve model performance across devices? Experiments using a Meibomian gland segmentation model.Frontiers in cell and developmental biology · 2022Article
- 2D Short-Time Fourier Transform for local morphological analysis of meibomian gland images.PloS one · 2022Article
- Meibomian glands visibility assessment through a new quantitative method.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2021Article
- Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography.Scientific reports · 2021Article
- An automated and multiparametric algorithm for objective analysis of meibography images.Quantitative imaging in medicine and surgery · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe present and validate a new methodology for analyzing, in an automated and objective fashion, infrared images of the meibomian glands (MG).
methodsThe developed algorithm consists of three main steps: selection of the region of interest, detection of MG, and analysis of MG morphometric parameters and dropout area (DOA). Additionally, a new approach to quantify the irregularity of MG is introduced. We recruited 149 adults from a general population. Infrared meibography, using Keratograph 5M, was performed. Images were assessed and graded subjectively (Meiboscore) by two experienced clinicians and objectively with the proposed automated method.
resultsThe correlation of subjective DOA assessment between the two clinicians was poor and the average percentage of DOA estimated objectively for each Meiboscore group did not lie within their limits. The objective assessment showed lower variability of meibography grading than that obtained subjectively. Additionally, a new grading scale of MG DOA that reduces intraclass variation is proposed. Reported values of MG length and width were inversely proportional to the DOA. Gland irregularity was objectively quantified.
conclusionsThe proposed automatic and objective method provides accurate estimates of the DOA as well as additional morphologic parameters that could add valuable information in MG dysfunction understanding and diagnosis. TRANSLATIONAL RELEVANCE: This approach highlights the shortcomings of currently used subjective methods, and provides the clinicians with an objective, quantitative and less variable alternative for assessing MG in a noninvasive and automated fashion. It provides a viable alternative to more time-consuming subjective methods.
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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.