AIM:To construct an artificial intelligence(AI)system based on convolutional neural network(CNN), which can automatically evaluate the morphological changes of meibomian gland(MG)in meibomian gland dysfunction(MGD)patients.

METHODS:The right eyes of 145 subjects who were treated at the Hangzhou Branch of the Eye Hospital of Wenzhou Medical University from January to November 2021 were selected for inclusion in the study. Meibography images of 60 of these subjects were randomly selected for AI training. The meibomian region and each MG in meibography were annotated and formed into datasets. The datasets were used for training and obtaining an AI system based on residual neural network(ResNet)combined with the U-Net model. The AI system was used to automatically analyze the MG morphological parameters in 85 subjects, including 53 patients with obstructive MGD and 32 volunteers with normal meibomian glands. The clinical indices including ocular surface disease index(OSDI), tear meniscus height, tear film break-up time(TBUT), corneal fluorescein staining, lid margin score, meiboscore, and meibomian gland expressibility score were also observed. The correlation between MG morphological parameters and clinical indices were analyzed.

RESULTS: After several iterations, we finally obtained an AI system with Intersection over Union of 92.0%. Using this AI system, we found that there was a significant correlation between the MG density in the upper eyelid with OSDI(r_s=-0.320), TBUT(r_s=0.484), lid margin score(r_s=-0.350), meiboscore(r_s=-0.749), and meibum expressibility score(r_s=0.425)(all P<0.05). The MG density in the lower eyelid was significantly correlated with OSDI(r_s=-0.420), TBUT(r_s= 0.598), lid margin score(r_s=-0.396), meiboscore(r_s=-0.720), and meibum expressibility score(r_s=0.438)(all P<0.05). The MG density in the total eyelid was significantly correlated with OSDI(r_s=-0.404), TBUT(r_s=0.601), lid margin score(r_s=-0.416), meiboscore(r_s=-0.805), and meibum expressibility score(r_s=0.480)(all P<0.05).

CONCLUSION:The AI system based on CNN in this study is an accurate and efficient MG morphological evaluation system, which can be conveniently used to evaluate the MG morphology of MGD patients quickly and accurately by using the MG density index established by us. MG density is a new quantitative index to evaluate meibomian gland atrophy, which is more accurate than meiboscore.