AIMC Topic: Meibomian Glands
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Performance of a novel multimodal large language model in ınterpreting meibomian glands quantitatively and qualitatively.
International ophthalmology
May 28, 2025
PURPOSE: To evaluate the performance of a multimodal large language model (LLM), Claude 3.5 Sonnet, in interpreting meibography images for Meibomian gland dropout grading and morphological abnormality detection.
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
Jan 13, 2025
SIGNIFICANCE: Optimal meibography utilization and interpretation are hindered due to poor lid presentation, blurry images, or image artifacts and the challenges of applying clinical grading scales. These results, using the largest image dataset analy...
Strip and boundary detection multi-task learning network for segmentation of meibomian glands.
Medical physics
Nov 26, 2024
BACKGROUND: Automatic segmentation of meibomian glands in near-infrared meibography images is basis of morphological parameter analysis, which plays a crucial role in facilitating the diagnosis of meibomian gland dysfunction (MGD). The special strip ...
Advances in artificial intelligence for meibomian gland evaluation: A comprehensive review.
Survey of ophthalmology
Jul 23, 2024
Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Co...
Predicting demographics from meibography using deep learning.
Scientific reports
Sep 20, 2022
This study introduces a deep learning approach to predicting demographic features from meibography images. A total of 689 meibography images with corresponding subject demographic data were used to develop a deep learning model for predicting gland m...
Automatic identification of meibomian gland dysfunction with meibography images using deep learning.
International ophthalmology
Sep 19, 2022
BACKGROUND: Artificial 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...
Automated quantification of meibomian gland dropout in infrared meibography using deep learning.
The ocular surface
Jun 24, 2022
PURPOSE: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.
A deep learning model established for evaluating lid margin signs with colour anterior segment photography.
Eye (London, England)
Jun 23, 2022
OBJECTIVES: To evaluate the feasibility of applying a deep learning model to identify lid margin signs from colour anterior segment photography.
Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography.
Scientific reports
Apr 7, 2021
Meibomian glands (MG) are large sebaceous glands located below the tarsal conjunctiva and the abnormalities of these glands cause Meibomian gland dysfunction (MGD) which is responsible for evaporative dry eye disease (DED). Accurate MG segmentation i...
Characterization of expressed human meibum using hyperspectral stimulated Raman scattering microscopy.
The ocular surface
Oct 11, 2018
PURPOSE: This study examined whether hyperspectral stimulated Raman scattering (hsSRS) microscopy can detect differences in meibum lipid to protein composition of normal and evaporative dry eye subjects with meibomian gland dysfunction.
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