EClinicalMedicine (Oct 2021)
Quantitative analysis of morphological and functional features in Meibography for Meibomian Gland Dysfunction: Diagnosis and Grading
Abstract
Background: To explore the performance of quantitative morphological and functional analysis in meibography images by an automatic meibomian glands (MGs) analyser in diagnosis and grading Meibomian Gland Dysfunction (MGD). Methods: A cross-sectional study collected 256 subjects with symptoms related to dry eye and 56 healthy volunteers who underwent complete ocular surface examination was conducted between January 1, 2019, and December 31, 2020. The 256 symptomatic subjects were classified into MGD group (n = 195) and symptomatic non-MGD group (n = 61). An automatic MGs analyser was used to obtained multi-parametric measurements in meibography images including the MGs area ratio (GA), MGs diameter deformation index (DI), MGs tortuosity index (TI), and MGs signal index (SI). Adjusted odds ratios (ORs) of the multi-parametric measurements of MGs for MGD, and the area under the receiver operating characteristic (AUC-ROC) curves of multi-parametric measurements for MGD diagnosing and grading were conducted. Findings: When consider age, sex, ocular surface condition together, the estimated ORs for DI was 1.62 (95% CI, 1.29-2.56), low-level SI was 24.34 (95% CI, 2.73-217.3), TI was 0.76(95% CI, 0.54-0.90), and GA was 0.86 (95% CI, 0.74-0.92) for MGD. The combination of DI-TI-GA-SI showed an AUC = 0.82 (P < 0.001) for discriminating MGD from symptomatic subjects. The DI had a higher AUC in identifying early-stage MGD (grade 1-2), while TI and GA had higher AUCs in moderate and advanced stages (grade 3-5). Merging DI-TI-GA showed the highest AUCs in distinguish MGD severities. Interpretation: The MGs area ratio, diameter deformation, tortuosity and signal intensity could be considered promising biomarkers for MGD diagnosis and objective grading. Funding: This work was supported by the Key-Area Research and Development Program of Guangdong Province (No. 2019B010152001), the National Natural Science Foundation of China under Grant (81901788) and Guangzhou Science and Technology Program (202002030412).
