The role of meibography in ocular surface diagnostics: A review

Influential Signs of Dry Eye-Related Ocular Symptoms in Participants With Unstable Tear Film

PURPOSE

The aim of this study was to investigate the factors influencing dry eye disease (DED)-related ocular symptoms in participants with short fluorescein tear break-up time (FTBUT).

METHODS

This cross-sectional study included 82 participants with short FTBUT (<10 seconds). Examinations included Ocular Surface Disease Index (OSDI), FTBUT, average noninvasive tear break-up time (NIBUTave), lid wiper epitheliopathy, lipid layer thickness, blink rate, partial blink, tear meniscus height, and meibomian gland (MG) evaluation which included ratio of residual MG area (RMGA) and MG grade in tarsal plates. One-way analysis of variance was used to detect differences between symptomatic tear film instability group (FTBUT <5 s, OSDI ≥13), asymptomatic tear film instability group (FTBUT <5 s, OSDI <13), and control group (FTBUT ≥5 s, OSDI <13). A bivariate correlation, partial correlation, and multiple linear regression analyses were used to identify major factors. Only the right eye was included.

RESULTS

Among the participants with FTBUT <5 seconds, symptomatic group showed less upper RMGA ( P < 0.001) and NIBUTave ( P = 0.010). OSDI was negatively associated with upper RMGA ( r = -0.450, P < 0.001) and NIBUTave ( r = -0.414, P = 0.001), and positively associated with upper MG grade ( r = 0.277, P = 0.027). Linear regression analysis showed that the upper RMGA significantly affected OSDI (B = -41.895, P = 0.001), while not significantly correlated with age, upper MG grade, and NIBUTave.

CONCLUSIONS

The upper RMGA might be the main factor affecting DED-related discomfort in participants with unstable tear film, indicating an early ocular change in DED.

Anterior segment optical coherence tomography meibography compared with keratograph meibography

PURPOSE

The aim of this study was to determine the feasibility of using readily accessible technology, anterior segment optical coherence tomography (AS-OCT), to detect and grade meibomian gland dropout and examine its interchangeability with the Oculus Keratograph 5M (K5M).

METHODS

A total of 30 participants (30 eyes) with a median age of 21 (range = 19-28 years) were recruited. Meibography was performed using two commercially available imaging devices to look at the structure of the meibomian glands and grade them subjectively in real time, and image analysis was used to quantify meibomian gland loss objectively. Gland loss as imaged by the two techniques was graded using the meiboscore grading schema. Test-retest reliability was determined with intraclass correlation coefficients (ICCs). Weighted kappa was used to evaluate agreement between the two imaging devices and four methods of image analysis. Spearman and Pearson correlation coefficients were used to determine the association of structural measurements between each of the techniques. The agreement between the two imaging techniques was determined with the Bland-Altman analysis.

RESULTS

Reliability of subjective grading was strong for AS-OCT (ICC: 0.92, 95% CI: 0.83-0.96, p < 0.001) and K5M (ICC: 0.96, 95% CI: 0.96-0.91, p = 0.001). Image analysis with ImageJ reliability was strong between the imaging devices (ICC: 0.84, 95% CI: 0.55-0.94, p < 0.001). Agreement between each subjective technique was fair, κ = 0.45 (95% CI: 0.17-0.73, p < 0.001) and a positive Spearman correlation was also observed (r = 0.52, p < 0.001). There was no significant difference between the mean meibomian gland loss measured with ImageJ between AS-OCT and K5M (0.92 ± 6.28, p = 0.26). The 95% limits of agreement were -12.45% to +14.04%.

CONCLUSION

These findings suggest subjective real-time grading of meibomian gland loss could be performed using readily available AS-OCT technology and that this method was interchangeable with the K5M.

Association of meibomian gland morphology with orifice plugging and lid margin thickening in meibomian gland dysfunction patients

PURPOSE

We sought to investigate the association of meibomian gland morphology with lid margin abnormalities in patients with meibomian gland dysfunction.

METHODS

This retrospective study included 368 eyes of 184 patients. Meibography was used to evaluate meibomian gland (MG) morphological features, such as dropout, distortion, thickened ratio and thinned ratio. Lid margin photography was used to evaluate lid margin abnormalities including orifice plugging, vascularity, irregularity and thickening. The association between MG morphological features and lid margin abnormalities was analyzed using a mixed linear model.

RESULTS

The study found a positive correlation between plugging of gland orifices grade and MG dropout grade in both the upper lids (B = 0.40, p = 0.007) and the lower lids (B = 0.55, p = 0.001). Plugging of gland orifices grade was also positively correlated to MG distortion grade in the upper lids (B = 0.75, p = 0.006). In the upper lids, MG thickened ratio increased first (B = 0.21, p = 0.003) and then decreased (B = - 0.14, p = 0.010) with a higher lid margin thickening grade. MG thinned ratio was negatively correlated with lid margin thickening (B = - 0.14, p = 0.002, B = - 0.13, p = 0.007). MG distortion grade decreased with lid margin thickening (B = - 0.61, p = 0.012).

CONCLUSION

Orifice plugging was correlated to meibomian gland distortion and dropout. Lid margin thickening was associated with meibomian gland thickened ratio, thinned ratio, and distortion. The study also suggested that distorted and thinned glands may be transitional phases between thickened glands and glands dropout.

Predicting an unstable tear film through artificial intelligence

Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence algorithms perform on clinical data related to dry eye disease. The data was processed and subjected to numerous machine learning classification algorithms with the aim to predict decreased tear film break-up time. Moreover, feature selection techniques (information gain and information gain ratio) were applied to determine which clinical factors contribute most to an unstable tear film. The applied machine learning algorithms outperformed baseline classifications performed with ZeroR according to included evaluation metrics. Clinical features such as ocular surface staining, meibomian gland expressibility and dropout, blink frequency, osmolarity, meibum quality and symptom score were recognized as important predictors for tear film instability. We identify and discuss potential limitations and pitfalls.

2D Short-Time Fourier Transform for local morphological analysis of meibomian gland images

Meibography is becoming an integral part of dry eye diagnosis. Being objective and repeatable this imaging technique is used to guide treatment decisions and determine the disease status. Especially desirable is the possibility of automatic (or semi-automatic) analysis of a meibomian image for quantification of a particular gland’s feature. Recent reports suggest that in addition to the measure of gland atrophy (quantified by the well-established “drop-out area” parameter), the gland’s morphological changes may carry equally clinically useful information. Here we demonstrate the novel image analysis method providing detailed information on local deformation of meibomian gland pattern. The developed approach extracts from every Meibomian image a set of six morphometric color-coded maps, each visualizing spatial behavior of different morphometric parameter. A more detailed analysis of those maps was used to perform automatic classification of Meibomian glands images. The method for isolating individual morphometric components from the original meibomian image can be helpful in the diagnostic process. It may help clinicians to see in which part of the eyelid the disturbance is taking place and also to quantify it with a numerical value providing essential insight into Meibomian gland dysfunction pathophysiology.

Automated quantification of meibomian gland dropout in infrared meibography using deep learning

PURPOSE

Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.

METHODS

A total of 1600 meibography images were captured in a clinical setting. 1000 images were precisely annotated with multiple revisions by investigators and graded 6 times by meibomian gland dysfunction (MGD) experts. Two deep learning (DL) models were trained separately to segment areas of the MG and eyelid. Those segmentation were used to estimate MG ratio and meiboscores using a classification-based DL model. A generative adversarial network was implemented to remove specular reflections from original images.

RESULTS

The mean ratio of MG calculated by investigator annotation and DL segmentation was consistent 26.23% vs 25.12% in the upper eyelids and 32.34% vs. 32.29% in the lower eyelids, respectively. Our DL model achieved 73.01% accuracy for meiboscore classification on validation set and 59.17% accuracy when tested on images from independent center, compared to 53.44% validation accuracy by MGD experts. The DL-based approach successfully removes reflection from the original MG images without affecting meiboscore grading.

CONCLUSIONS

DL with infrared meibography provides a fully automated, fast quantitative evaluation of MG morphology (MG Segmentation, MG area, MG ratio, and meiboscore) which are sufficiently accurate for diagnosing dry eye disease. Also, the DL removes specular reflection from images to be used by ophthalmologists for distraction-free assessment.

Latest developments in meibography: A review

Meibography is a visualisation technique that has been used for over 40 years. There have been significant improvements in image quality, examination technique and image interpretation over this period. Although meibography has received sporadic reviews in the past, an updated review is timely due to the rapid recent rise of relevant technology and advances in both image processing and artificial intelligence. The primary aim of this paper is to review recent research into Meibomian gland imaging and update the community about the most relevant technologies and approaches used in the field.

Meibomian Glands and Tear Film Findings in Type 2 Diabetic Patients: A Cross-Sectional Study

Background

The characteristics of the meibomian gland and tear film in patients with type 2 diabetes (T2D) with different glycemic control levels and diabetic durations remain largely unexplored. This study aimed to identify the association of dry eye and meibomian gland dysfunction (MGD) in T2D.

Materials and Methods

Ninety-nine patients with type 2 diabetes mellitus (DM group), 33 dry eye patients without diabetes mellitus (DE group), and 40 normal subjects (NC group) were recruited for this study. Participants were evaluated with an Ocular Surface Disease Index (OSDI) questionnaire, tear film breakup time (BUT), the Schirmer I test (SIT), corneal fluorescein staining (FL), lipid layer thickness (LLT), and MGD parameters. Glycosylated hemoglobin (HbA_1c) and duration of diabetes were recorded.

Results

The SIT value in the DM group was higher than that of the DE group (p < 0.05). The BUT and LLT were lower, and MGD parameters were higher in the DM group than those of the DE and NC groups (p < 0.05). In the DM group, 47 patients were diagnosed with dry eye (DM + DE group), whereas 40 patients without dry eye were categorized as the DM − DE group. The SIT, BUT, and LLT values in the DM − DE group were higher (p < 0.01), and MGD parameters were lower (p < 0.01) in the DM − DE group than those of the DM + DE group. The MGD parameters were higher in the DM − DE group than those in the NC group (p < 0.05). The HbA_1c levels were correlated with OSDI, BUT, LLT, FL, and MGD parameters (p < 0.001) in the DM group. However, in patients with low HbA_1c, normal SIT value, and low OSDI, the MGD parameters were higher than those in the NC group (p < 0.05). The duration of diabetes positively correlated with MGD parameters (p < 0.001).

Conclusion

Asymptomatic MGD may be an early sign of dry eye and ocular discomfort in T2D. The MGD parameters were associated with the HbA_1c level and diabetic duration.

Prevalence of symptomatic dry eye disease with associated risk factors among medical students at Chiang Mai University due to increased screen time and stress during COVID-19 pandemic

Dry eye disease (DED) is one of the most common ophthalmological disorders, resulting from several systemic and ocular etiologies including meibomian gland dysfunction (MGD). During the COVID-19 pandemic, medical students are among the high-risk group for DED, mainly due to the increasing use of a visual display terminal (VDT) for online lectures and psychological stress from encountering several changes. Our study aimed to explore the prevalence of DED using the symptom-based definition and potential risk factors in medical students. This is a prospective cross-sectional study that included medical students at Chiang Mai University between November 2020 and January 2021. All participants were assessed using the Ocular Surface Disease Index (OSDI) questionnaire, the Thai version of the 10-Item Perceived Stress Scale-10 (T-PSS-10), the LipiView® II interferometer, and an interview for other possible risk factors. Overall, 528 participants were included in the study; half of the participants were female. The prevalence of DED was 70.8%. In the univariate analysis, female sex, contact lens wear, and T-PSS-10 stress scores were significantly higher in the DED group (P = 0.002, 0.002, and <0.001, respectively). Moreover, participants with severe DED were likely to have higher meibomian gland tortuosity but not statistically significant. In the multivariate analysis, contact lens use and T-PSS-10 score were significant risk factors associated with the severity of DED. In conclusions, the prevalence of DED in medical students was as high as 70.8%. Contact lens use and psychological stress evaluated using the T-PSS-10 questionnaire had a significant correlation with a risk of DED. Female gender and duration of VDT use were also associated. Most of the risk factors were modifiable and may be used as initial management in patients with DED.

Structural and Functional Evaluation of Meibomian Glands and Its Correlation With Dry Eye Parameters in Stevens-Johnson Syndrome

PURPOSE

To evaluate the structural and functional changes of the meibomian gland and correlate with subjective and other objective dry eye parameters in Stevens-Johnson syndrome (SJS) patients.

DESIGN

Prospective cross-sectional study.

METHODS

This study recruited 60 patients (120 eyes) with SJS and chronic ocular sequelae. All patients underwent evaluation with a Standard Patient Evaluation of Eye Dryness (SPEED) questionnaire, tear osmolarity, Keratograph 5M, LipiView, Schirmer's 1, corneal staining, Meibum Expression score (MES, 0-3), and Meibum Quality score (MQS, 0-3). The meibomian gland dropout area (meiboscore) was calculated for both lids and graded on a 4-point scale from 0 to 3.

RESULTS

The mean age of patients (n = 60) was 31.08 ± 12.94 years; 25 were males. The commonest cause for SJS, in 51 patients (85%), was drug reaction. The mean tear osmolarity, lipid layer thickness (LLT), Schirmer's test-1, and tear break-up time was 322.70 ± 17.82 mOsm/L, 53.07 ± 27.0 nm, 6.62 ± 5.74 mm, 3.38 ± 1.90 seconds, respectively. Out of 240 eyelids (both upper and lower), 160 (65%) eyelids revealed severe meibomian gland loss. The mean upper and lower lid gland loss was 77.36 ± 28.82% and 76.65 ± 29.33%, respectively, Seventy-four eyelids (61%) had no expressible glands. Meiboscore showed positive correlation with SPEED (P < .001), corneal staining scores (P < .001), MES (P < .001), and MQS (P < .001). The LLT negatively correlation with SPEED (P < .01), meiboscore (P < .001), MES (P < .01), and MQS (P < .001).

CONCLUSION

Significant alterations in anatomical and functional aspects of the meibomian gland are seen in SJS. The high meiboscore, MES, MQS, and decreased LLT contributed to the worsening dry eye state, as seen by their correlation with other dry eye parameters. This study highlights the need to evaluate meibomian gland structure and function in patients with chronic ocular sequelae of SJS.

Advances in Dry Eye Disease Examination Techniques

Dry eye-related ocular surface examination is very important in the diagnosis and treatment of dry eye disease. With the recent advances in science and technology, dry eye examination techniques have progressed rapidly, which has greatly improved dry eye diagnoses and treatment. However, clinically, confusion remains about which examination to choose, how to ensure the repeatability of the examination, and how to accurately interpret the examination results. In this review, we systematically evaluate previous examinations of dry eye, analyze the latest views and research hotspots, and provide a reference for the diagnosis and management of dry eye.

Proposed Algorithm for Management of Meibomian Gland Dysfunction Based on Noninvasive Meibography

Although the pathophysiology of meibomian gland dysfunction (MGD) remains incompletely understood, many treatment options have recently become available. According to an international workshop report, treatment selection for MGD should be based on a comprehensive stage classification dependent on ocular symptoms, lid margin abnormalities, meibum grade, and ocular surface staining. However, it is often difficult to evaluate all parameters required for such classification in routine clinical practice. We have now retrospectively evaluated therapeutic efficacy in MGD patients who received five types of treatment in the clinic setting: (1) meibocare (application of a warm compress and practice of lid hygiene), (2) meibum expression plus meibocare, (3) azithromycin eyedrops plus meibocare, (4) thermal pulsation therapy plus meibocare, or (5) intense pulsed light (IPL) therapy plus meibocare. Patients in each treatment group were classified into three subsets according to the meiboscore determined by noncontact meibography at baseline. Eyes in the IPL group showed improvement even if the meiboscore was high (5 or 6), whereas meibocare tended to be effective only if the meiboscore was low (1 or 2). The meiboscore may thus serve to guide selection of the most appropriate treatment in MGD patients. Prospective studies are warranted to confirm these outcomes.