We aimed to establish an artificial intelligence (AI) system based on deep learning and transfer learning for meibomian gland (MG) segmentation and evaluate the efficacy of MG density in the diagnosis of MG dysfunction (MGD). First, 85 eyes of 85 subjects were enrolled for AI system-based evaluation effectiveness testing. Then, from 2420 randomly selected subjects, 4006 meibography images (1620 upper eyelids and 2386 lower eyelids) graded by three experts according to the meiboscore were analyzed for MG density using the AI system. The updated AI system achieved 92% accuracy (intersection over union, IoU) and 100% repeatability in MG segmentation after 4 h of training. The processing time for each meibography was 100 ms. We discovered a significant and linear correlation between MG density and ocular surface disease index questionnaire (OSDI), tear break-up time (TBUT), lid margin score, meiboscore, and meibum expressibility score (all p < 0.05). The area under the curve (AUC) was 0.900 for MG density in the total eyelids. The sensitivity and specificity were 88% and 81%, respectively, at a cutoff value of 0.275. MG density is an effective index for MGD, particularly supported by the AI system, which could replace the meiboscore, significantly improve the accuracy of meibography analysis, reduce the analysis time and doctors’ workload, and improve the diagnostic efficiency.

The purpose of this study was to compare the differences between uneven meibomian gland (MG) atrophy with and without Demodex infestation based on the index of uneven atrophy score (UAS). In this retrospective cohort study, 158 subjects were recruited, including 66 subjects in the Demodex-positive MGD group, 49 subjects in the Demodex-negative MGD group, and 43 subjects as normal control. No significant difference was verified in OSDI, TMH, TBUT, CFS, lid margin score, and meibograde (all p > 0.05) between the Demodex-positive MGD group and the Demodex-negative MGD group. The UAS index of the upper eyelid or both eyelids was significantly higher in the Demodex-positive group in comparison with the normal control group and Demodex-negative group and the difference was statistically significant between the three groups. The UAS was significantly positive correlation with OSDI (r = 0.209, p < 0.05), lid margin score (r = 0.287, p < 0.001), and meibograde (r = 0.356, p < 0.001), which has a significant negative correlation with TBUT (r = −0.248, p < 0.05). Thus, Demodex infestation can cause uneven MG atrophy and we propose a novel index of UAS, which is used to evaluate uneven atrophy of MGs and as a morphological index of Demodex infestation.