AIMC Topic: Meibomian Gland Dysfunction

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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.
Ultrasonography Tears Male Middle Aged Meibomian Glands Meibomian Gland Dysfunction Diagnostic Techniques, Ophthalmological Large Language Models Female Humans Adult Language
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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...
Algorithms Reproducibility of Results Humans Neural Networks, Computer Meibomian Glands Meibomian Gland Dysfunction Convolutional Neural Networks Diagnostic Techniques, Ophthalmological
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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...
Meibomian Glands Meibomian Gland Dysfunction Tomography, Optical Coherence Diagnostic Techniques, Ophthalmological Humans Artificial Intelligence Microscopy, Confocal
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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...
Meibomian Gland Dysfunction Eyelid Diseases Meibomian Glands Dry Eye Syndromes Deep Learning Humans Tears Artificial Intelligence
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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.
Humans Deep Learning Tears Dry Eye Syndromes Meibomian Glands Ophthalmologists Meibomian Gland Dysfunction Eyelid Diseases
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Deep Neural Network-Based Method for Detecting Obstructive Meibomian Gland Dysfunction With in Vivo Laser Confocal Microscopy.

Cornea Jun 1, 2020
PURPOSE: To evaluate the ability of deep learning (DL) models to detect obstructive meibomian gland dysfunction (MGD) using in vivo laser confocal microscopy images.
Meibomian Glands Meibomian Gland Dysfunction Follow-Up Studies Middle Aged Constriction, Pathologic Deep Learning Adult Female ROC Curve Adolescent Neural Networks, Computer Humans Male Child Aged Microscopy, Confocal Retrospective Studies Aged, 80 and over Young Adult
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