Evaluation of ocular psoriasis with meibography

Abstract Background: Previous studies has shown that dry eye test abnormalities, meibomian gland dysfunction (MGD), may occur in psoriasis. Objectives: The authors aimed to evaluate the dry eye disease (DED), MGD, in psoriasis patients with meibography which is a current, objective, noninvasive method for patients with meibomian gland diseases, to investigate the relationship between disease severity and ocular involvement. Methods: This study included 50 participants with psoriasis and 50 healthy individuals. All subjects were examined by the same dermatologist and referred for ophthalmological examination including meibomian gland obstruction, lid margin alterations assessment, ocular surface disease index assessment, tear film break-up time test, Schirmer test, corneal conjunctival fluorescein staining assessment. Additionally, upper and lower lids were evaluated for meibomian gland loss with meibography. Results: MGD (28%), meibomian gland loss (MGL) (29.5%), upper meiboscore (0.61 ± 0.81), lower meiboscore (0.46 ± 0.61), DED (22%) were significantly higher in the psoriasis group compared with the control group (p = 0.008, p < 0.001, p = 0.027, p = 0.041, p = 0.044, respectively). There was a significant relationship between MGD and psoriasis area severity index (PASI) (p = 0.015, Odds Ratio = 1.211). There was a significant positive relationship between MGL with PASI (p < 0.001, r = 608) and psoriasis duration (p < 0.001, r = 0.547). Study limitations: Smaller study group and inability to detect quality changes of meibum with meibography were limitations of the study. Conclusions: Psoriasis may affect the meibomian gland morphology, may cause structural changes in meibomian glands, and as a result of these may cause MGD and DED. Therefore, ophthalmologists and dermatologists should be aware of this situation and co-evaluate the patients in this respect.

Infrared Imaging Meibomian Gland Segmentation System Based on Deep Learning / 中国医疗器械杂志

In order to better assist doctors in the diagnosis of dry eye and improve the ability of ophthalmologists to recognize the condition of meibomian gland, a meibomian gland image segmentation and enhancement method based on Mobile-U-Net network was proposed. Firstly, Mobile-Net is used as the coding part of U-Net for down sampling, and then features are extracted and fused with the features in decoder to guide image segmentation. Secondly, the segmentation of meibomian gland region is enhanced to assist doctors to judge the condition. Thirdly, a large number of meibomian gland images are collected to train and verify the semantic segmentation network, and the clarity evaluation index is used to verify the meibomian gland enhancement effect. The experimental results show that the similarity coefficient of the proposed method is stable at 92.71%, and the image clarity index is better than the similar dry eye detection instruments on the market.