A Novel Meibomian Gland Morphology Analytic System Based on a Convolutional Neural Network

Abstract

Meibomian glands dysfunction (MGD) is the main cause of dry eyes. Biological parameters of meibomian gland (MG) such as height, tortuosity and the degree of atrophy are closely related to its function. However, Thus, an effective quantitative diagnostic tool is needed for clinical diagnosis. Automatic quantification of MGs' morphological features could be a challenging task and play an important role in MGD diagnosis and classification. Our main objective is to develop an artificial intelligence (AI) system for evaluating MGs' morphology and explore the relationship between the morphological parameters and functions. We proposed a novel MGs extraction method based on convolutional neural network (CNN) with enhanced mini U-Net. A prospective study was conducted, 120 subjects were included and taken meibography. The training and validation sets encompassed 60 subjects; and the test set consisted of other 60 subjects with comprehensive examinations for ocular surface disease index questionnaire (OSDI), tear meniscus height (TMH), tear break-up time (TBUT), corneal fluorescein staining (CFS), lid margin score, and meibum expressibility score. The algorithm effectively extracted MGs from meibography even with this small training sample. As a result, while the intersection over union (IoU) achieved 0.9077, the repeatability was 100%. The processing time for each image was 100ms. Using this method, the investigators identified a significant and linear correlation between MG morphology and clinical parameters. This study provided a new method for quantification of MGs' morphological features obtained by meibography, which has advantages in reducing analysis time, improving diagnostic efficiency, and assisting ophthalmologists with limited clinical expertise.

Keywords

• Deep learning
• convolutional neural network (CNN)
• meibomian gland dysfunction (MGD)
• meibography