Purpose We present and validate a new methodology for analyzing, in an automated and objective fashion, infrared images of the meibomian glands (MG). Methods The 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. Results The 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. Conclusions The 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.
Purpose: To objectively and quantitatively characterize meibomian gland morphology and to investigate the influence of morphological variations on gland function and ocular surface and tear film parameters. Methods: One hundred fifty subjects were enrolled. The examinations included tear osmolarity, tear meniscus height, bulbar conjunctival hyperemia, noninvasive tear film breakup time, lid margin thickness, foam secretion, meibomian gland expressibility, count of functioning glands, corneal and conjunctival staining, fluorescein breakup time, lid wiper epitheliopathy, and Schirmer test. Patient symptoms were assessed using the Ocular Surface Disease Index questionnaire. Images from noncontact meibography were analyzed using an automated method that objectively estimates dropout area, number of glands, gland length and width, and gland irregularity. Results: Gland irregularity highly correlated with dropout area (r = 20.4, P , 0.001) and showed significant partial correlations with fluorescein breakup time (r = 0.162, P = 0.049) and the Ocular Surface Disease Index questionnaire (r = 20.250, P = 0.002) Subjects with dropout area ,32% were divided into 2 groups: high and low irregularity. Gland expressibility was statistically significantly different between the 2 groups (U = 319.5, P = 0.006). In the high irregularity group, gland irregularity correlated with the Schirmer test (r = 0.530, P = 0.001) and corneal fluorescein staining (r = 20.377, P = 0.021). Conclusions: Automated morphological analysis of meibomian gland structure provides additional quantitative and objective information regarding gland morphology. The link between dropout area and gland function is not clear. Assessment of gland irregularity might better predict gland function and its effects on ocular surface and tear film parameters.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.