Meibomian gland alterations in allergic conjunctivitis: insights from a novel quantitative analysis algorithm

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence in Healthcare

Background:

• Allergic conjunctivitis (AC) is a common ocular surface inflammatory condition.
• Meibomian gland (MG) dysfunction is increasingly recognized as a contributor to ocular surface disease.
• Quantitative analysis of MG structure changes in AC is crucial for understanding disease mechanisms.

Purpose of the Study:

• To investigate structural changes in meibomian glands (MGs) in patients with allergic conjunctivitis (AC).
• To employ an intelligent quantitative analysis algorithm for precise MG structure evaluation.
• To explore the correlation between MG structural alterations and clinical parameters in AC.

Main Methods:

• Infrared meibography was performed on 252 eyes with AC and 200 control eyes.
• A deep learning-based quantitative algorithm analyzed MG images for length, area, dropout ratio, and deformation.
• Clinical parameters including tear meniscus height, tear break-up time (TBUT), conjunctival hyperemia, and OSDI scores were assessed.

Main Results:

• AC patients exhibited shorter average MG length and reduced central MG area compared to controls.
• Lower tear meniscus height, shorter TBUT, and increased conjunctival hyperemia were observed in AC patients.
• Higher OSDI scores were recorded in AC patients, with gland dropout ratio correlating with gender, age, and OSDI.

Conclusions:

• Intelligent quantitative analysis reveals significant MG structural changes in AC, notably reduced gland length and area.
• AC impacts ocular surface parameters like tear film stability and tear meniscus height.
• The findings highlight the structural basis of MG dysfunction in allergic conjunctivitis.