A Novel Automated Approach for Infrared-Based Assessment of Meibomian Gland Morphology

Machine learning-based prediction of tear osmolarity for contact lens practice

PURPOSE

This study addressed the utilisation of machine learning techniques to estimate tear osmolarity, a clinically significant yet challenging parameter to measure accurately. Elevated tear osmolarity has been observed in contact lens wearers and is associated with contact lens-induced dry eye, a common cause of discomfort leading to discontinuation of lens wear.

METHODS

The study explored machine learning, regression and classification techniques to predict tear osmolarity using routine clinical parameters. The data set consisted of 175 participants, primarily healthy subjects eligible for soft contact lens wear. Various clinical assessments were performed, including symptom assessment with the Ocular Surface Disease Index and 5-Item Dry Eye Questionnaire (DEQ-5), tear meniscus height (TMH), tear osmolarity, non-invasive keratometric tear film break-up time (NIKBUT), ocular redness, corneal and conjunctival fluorescein staining and Meibomian glands loss.

RESULTS

The results revealed that simple linear regression was insufficient for accurate osmolarity prediction. Instead, more advanced regression models achieved a moderate level of predictive power, explaining approximately 32% of the osmolarity variability. Notably, key predictors for osmolarity included NIKBUT, TMH, ocular redness, Meibomian gland coverage and the DEQ-5 questionnaire. In classification tasks, distinguishing between low (<299 mOsmol/L), medium (300-307 mOsmol/L) and high osmolarity (>308 mOsmol/L) levels yielded an accuracy of approximately 80%. Key parameters for classification were similar to those in regression models, emphasising the importance of NIKBUT, TMH, ocular redness, Meibomian glands coverage and the DEQ-5 questionnaire.

CONCLUSIONS

This study highlights the potential benefits of integrating machine learning into contact lens research and practice. It suggests the clinical utility of assessing Meibomian glands and NIKBUT in contact lens fitting and follow-up visits. Machine learning models can optimise contact lens prescriptions and aid in early detection of conditions like dry eye, ultimately enhancing ocular health and the contact lens wearing experience.

Temporal variations in meibomian gland structure-A pilot study

PURPOSE

To investigate whether there is a measurable change in meibomian gland morphological characteristics over the course of a day (12 h) and over a month.

METHODS

The study enrolled 15 participants who attended a total of 11 study visits spanning a 5-week period. To assess diurnal changes in meibomian glands, seven visits were conducted on a single day, each 2 h apart. For monthly assessment, participants attended an additional visit at the same time of the day every week for three consecutive weeks. Meibography using the LipiView® II system was performed at each visit, and meibomian gland morphological parameters were calculated using custom semi-automated software. Specifically, six central glands were analysed for gland length ratio, gland width, gland area, gland intensity and gland tortuosity.

RESULTS

The average meibomian gland morphological metrics did not exhibit significant changes during the course of a day or over a month. Nonetheless, certain individual gland metrics demonstrated notable variation over time, both diurnally and monthly. Specifically, meibomian gland length ratio, area, width and tortuosity exhibited significant changes both diurnally and monthly when assessed on a gland-by-gland basis.

CONCLUSIONS

Meibomian glands demonstrated measurable structural change over short periods of time (hours and days). These results have implications for innovation in gland imaging and for developing precision monitoring of gland structure to assess meibomian gland health more accurately.

A Deep Learning Approach for Meibomian Gland Appearance Evaluation

Purpose

To develop and evaluate a deep learning algorithm for Meibomian gland characteristics calculation.

Design

Evaluation of diagnostic technology.

Subjects

A total of 1616 meibography images of both the upper (697) and lower (919) eyelids from a total of 282 individuals.

Methods

Images were collected using the LipiView II device. All the provided data were split into 3 sets: the training, validation, and test sets. Data partitions used proportions of 70/10/20% and included data from 2 optometry settings. Each set was separately partitioned with these proportions, resulting in a balanced distribution of data from both settings. The images were divided based on patient identifiers, such that all images collected for one participant could end up only in one set. The labeled images were used to train a deep learning model, which was subsequently used for Meibomian gland segmentation. The model was then applied to calculate individual Meibomian gland metrics. Interreader agreement and agreement between manual and automated methods for Meibomian gland segmentation were also carried out to assess the accuracy of the automated approach.

Main Outcome Measures

Meibomian gland metrics, including length ratio, area, tortuosity, intensity, and width, were measured. Additionally, the performance of the automated algorithms was evaluated using the aggregated Jaccard index.

Results

The proposed semantic segmentation-based approach achieved average aggregated Jaccard index of mean 0.4718 (95% confidence interval [CI], 0.4680-0.4771) for the 'gland' class and a mean of 0.8470 (95% CI, 0.8432-0.8508) for the 'eyelid' class. The result for object detection-based approach was a mean of 0.4476 (95% CI, 0.4426-0.4533). Both artificial intelligence-based algorithms underestimated area, length ratio, tortuosity, widthmean, widthmedian, width10th, and width90th. Meibomian gland intensity was overestimated by both algorithms compared with the manual approach. The object detection-based algorithm seems to be as reliable as the manual approach only for Meibomian gland width10th calculation.

Conclusions

The proposed approach can successfully segment Meibomian glands; however, to overcome problems with gland overlap and lack of image sharpness, the proposed method requires further development. The study presents another approach to utilizing automated, artificial intelligence-based methods in Meibomian gland health assessment that may assist clinicians in the diagnosis, treatment, and management of Meibomian gland dysfunction.

Financial Disclosures

The authors have no proprietary or commercial interest in any materials discussed in this article.

Evaluation of Meibomian gland structure and appearance after therapeutic Meibomian gland expression

CLINICAL RELEVANCE

Evaluating how Meibomian glands can change in appearance has the potential to advance the understanding of Meibomian gland health and may lead to enhanced diagnosis and therapy.

BACKGROUND

This work aimed to investigate Meibomian gland appearance after therapeutic Meibomian gland expression.

METHODS

Fifteen subjects attended three study visits over a two-week period. Meibography was performed before and after therapeutic Meibomian gland expression, the following day, and 2 weeks after expression. Six central glands were used to calculate Meibomian gland morphological parameters such as gland length ratio, gland width, gland area, gland tortuosity, and gland contrast. A custom semi-automated image analysis software was used to calculate Meibomian gland metrics. Furthermore, a high-resolution imaging system was developed to capture clear images of the Meibomian glands, free of any artefacts, which were used for precise calculations of Meibomian gland contrast.

RESULTS

The expression procedure had a significant impact on Meibomian gland contrast and length ratio immediately afterwards. The least square mean difference (95% CI) from baseline for Michelson contrast was -0.006 (-0.010, -0.001) and -1.048 (-2.063, -0.033) for simple contrast. The least square mean ratio of the gland length ratio immediately after the expression to baseline was 0.758 (0.618, 0.931).

CONCLUSIONS

Following therapeutic expression, Meibomian glands exhibit reduced brightness and length. However, within 24 h, they appear to recover and return to their baseline state, indicating a relatively short recovery time. This sheds light on whether meibography is solely focused on capturing gland structure or if it also captures acinar activity. The hyperreflective properties of lipids suggest that the decrease in contrast observed after expression could be attributed to a reduction in the visualisation of acini activity. A decrease in Meibomian gland length ratio implies that the loss of gland structure following treatment may be indicative of a temporary structural alteration.

Assessment of meibomian gland drop-out and visibility through a new quantitative method in scleral lens wearers: A one-year follow-up study

OBJECTIVES

To validate a previously developed algorithm based on the visibility of meibomian gland images obtained with Cobra fundus camera and to assess the changes in meibomian glands in scleral lens wearers over one year of lens wear.

METHODS

Infrared meibography was obtained from the upper eyelid using the Cobra fundus camera in forty-three volunteers (34.2 ± 10.1 years). Meibographies were classified into 3 groups: Group 1 = good subjective gland visibility and gland drop-out < 1/3 of the total area; Group 2 = low visibility and gland drop-out < 1/3; and Group 3 = low visibility and gland drop-out > 1/3. Meibomian gland visibility metrics were then calculated using the developed algorithm from the pixel intensity values of meibographies. Repeatability of new metrics and their correlations with gland drop-out were assessed. Meibographies and ocular symptoms were also assessed after 1 year of scleral lens wear in 29 subjects.

RESULTS

Gland drop-out percentage was not statistically different between groups 1 and 2 (p = 0.464). Nevertheless, group 1 showed higher grey pixel intensity values than the other groups. Statistically significant correlations were found between gland visibility metrics and gland drop-out percentage. Repeatability was acceptable for all metrics, coefficient of variation achieving values between 0.52 and 3.18. While ocular symptoms decreased with scleral lens wear (p < 0.001), no statistically significant differences were found in gland drop-out percentage (p = 0.157) and gland visibility metrics (p > 0.217).

CONCLUSIONS

The proposed method can assess meibomian gland visibility in an objective and repeatable way. Scleral lens wear appears to not adversely affect meibomian gland drop-out and visibility while might improve dry eye symptoms after one year of lens wear. These preliminary results should be confirmed with a control group.

The efficacy of ocular surface assessment approaches in evaluating dry eye treatment with artificial tears

This study evaluates the effectiveness of objective techniques in assessing dry eye disease (DED) treatment compared with the subjective assessment commonly used in clinical practice. Thirty subjects were recruited for two visits separated by 28(± 3) days of treatment with artificial tears. A buttery of common subjective assessment methods were accompanied by a set of objective techniques including measurement of noninvasive tear film break-up time (NIBUT), lipid layer thickness (LLT), and quantitative evaluation of tear film surface quality and dynamics (TFD). Additionally, meibography was performed. Two commercially available videokeratoscopes and a prototype of a lateral shearing interferometer were used for the measurements. Both subjective and objective techniques showed a positive effect of artificial tears in DED treatment. Statistically significant improvements were observed in subjective symptoms (from P < 0.001 for Ocular Surface Disease Index, OSDI to p = 0.019 for tearing), conjunctival redness (P = 0.022), ocular staining (P = 0.012), fluorescein tear film break-up time (P = 0.015), NIBUT (P = 0.037), LLT (P < 0.001), and TFD (P = 0.048). In general, weak or statistically insignificant correlations were observed between subjective and objective assessment methods. The apparent lack of correlation between these methods might indicate the complementary character of objective techniques that likely assess other characteristics of ocular surface health than those assessed subjectively.

Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease

Dry eye disease (DED) is one of the most common diseases worldwide that can lead to a significant impairment of quality of life. The diagnosis and treatment of the disease are often challenging because of the lack of correlation between the signs and symptoms, limited reliability of diagnostic tests, and absence of established consensus on the diagnostic criteria. The advancement of machine learning, particularly deep learning technology, has enabled the application of artificial intelligence (AI) in various anterior segment disorders, including DED. Currently, many studies have reported promising results of AI-based algorithms for the accurate diagnosis of DED and precise and reliable assessment of data obtained by imaging devices for DED. Thus, the integration of AI into clinical approaches for DED can enhance diagnostic and therapeutic performance. In this review, in addition to a brief summary of the application of AI in anterior segment diseases, we will provide an overview of studies regarding the application of AI in DED and discuss the recent advances in the integration of AI into the clinical approach for DED.

Is histogram manipulation always beneficial when trying to improve model performance across devices? Experiments using a Meibomian gland segmentation model

Meibomian gland dysfunction (MGD) is caused by abnormalities of the meibomian glands (MG) and is one of the causes of evaporative dry eye (DED). Precise MG segmentation is crucial for MGD-related DED diagnosis because the morphological parameters of MG are of importance. Deep learning has achieved state-of-the-art performance in medical image segmentation tasks, especially when training and test data come from the same distribution. But in practice, MG images can be acquired from different devices or hospitals. When testing image data from different distributions, deep learning models that have been trained on a specific distribution are prone to poor performance. Histogram specification (HS) has been reported as an effective method for contrast enhancement and improving model performance on images of different modalities. Additionally, contrast limited adaptive histogram equalization (CLAHE) will be used as a preprocessing method to enhance the contrast of MG images. In this study, we developed and evaluated the automatic segmentation method of the eyelid area and the MG area based on CNN and automatically calculated MG loss rate. This method is evaluated in the internal and external testing sets from two meibography devices. In addition, to assess whether HS and CLAHE improve segmentation results, we trained the network model using images from one device (internal testing set) and tested on images from another device (external testing set). High DSC (0.84 for MG region, 0.92 for eyelid region) for the internal test set was obtained, while for the external testing set, lower DSC (0.69-0.71 for MG region, 0.89-0.91 for eyelid region) was obtained. Also, HS and CLAHE were reported to have no statistical improvement in the segmentation results of MG in this experiment.

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 Gland Density: An Effective Evaluation Index of Meibomian Gland Dysfunction Based on Deep Learning and Transfer Learning

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.

Artificial intelligence in dry eye disease

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term 'AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.

Diagnostic Capability of a New Objective Method to Assess Meibomian Gland Visibility

SIGNIFICANCE

The diagnosis of dry eye disease and meibomian gland dysfunction (MGD) is challenging. Measuring meibomian gland visibility may provide an additional objective method to diagnose MGD.

PURPOSE

This study aimed to evaluate the ability of new metrics to better diagnose MGD, based on measuring meibomian gland visibility.

METHODS

One hundred twelve healthy volunteers (age, 48.3 ± 27.5 years) were enrolled in this study. Ocular surface parameters were measured using the Oculus Keratograph 5M (Oculus GmbH, Wetzlar). Subjects were classified according to the presence or absence of MGD. New metrics based on the visibility of the meibomian glands were calculated and later compared between groups. The diagnostic ability of ocular surface parameters and gland visibility metrics was studied through receiver operating characteristic curves. Logistic regression was used to obtain the combined receiver operating characteristic curve of the metrics with the best diagnostic ability.

RESULTS

Statistically significant differences were found between groups for all ocular surface parameters and new gland visibility metrics, except for the first noninvasive keratograph breakup time and gland expressibility. New gland visibility metrics showed higher sensitivity and specificity than did current single metrics when their diagnostic ability was assessed without any combination. The diagnostic capability increased when gland visibility metrics were incorporated into the logistic regression analysis together with gland dropout percentage, tear meniscus height, dry eye symptoms, and lid margin abnormality score (P < .001). The combination of median pixel intensity of meibography gray values and the aforementioned ocular surface metrics achieved the highest area under the curve (0.99), along with excellent sensitivity (1.00) and specificity (0.93).

CONCLUSIONS

New meibomian gland visibility metrics are more powerful to diagnose MGD than current single metrics and can serve as a complementary tool for supporting the diagnosis of MGD.

Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography

Meibomian glands (MG) are large sebaceous glands located below the tarsal conjunctiva and the abnormalities of these glands cause Meibomian gland dysfunction (MGD) which is responsible for evaporative dry eye disease (DED). Accurate MG segmentation is a key prerequisite for automated imaging based MGD related DED diagnosis. However, Automatic MG segmentation in infrared meibography is a challenging task due to image artifacts. A deep learning-based MG segmentation has been proposed which directly learns MG features from the training image dataset without any image pre-processing. The model is trained and evaluated using 728 anonymized clinical meibography images. Additionally, automatic MG morphometric parameters, gland number, length, width, and tortuosity assessment were proposed. The average precision, recall, and F1 score were achieved 83%, 81%, and 84% respectively on the testing dataset with AUC value of 0.96 based on ROC curve and dice coefficient of 84%. Single image segmentation and morphometric parameter evaluation took on average 1.33 s. To the best of our knowledge, this is the first time that a validated deep learning-based approach is applied in MG segmentation and evaluation for both upper and lower eyelids.

An automated and multiparametric algorithm for objective analysis of meibography images

Background

Meibography is a non-contact imaging technique used by ophthalmologists and eye care practitioners to acquire information on the characteristics of meibomian glands. One of its most important applications is to assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD). As the artificial qualitative analysis of meibography images can lead to low repeatability and efficiency, automated and quantitative evaluation would greatly benefit the image analysis process. Moreover, since the morphology and function of meibomian glands varies at different stages of MGD, multiparametric analysis offering more comprehensive information could help in discovering subtle changes to glands during MGD progression. Therefore, an automated and multiparametric objective analysis of meibography images is urgently needed.

Methods

An algorithm was developed to perform multiparametric analysis of meibography images with fully automatic and repeatable segmentation based on image contrast enhancement and noise reduction. The full architecture can be divided into three steps: (I) segmentation of the tarsal conjunctiva area as the region of interest (ROI); (II) segmentation and identification of glands within the ROI; and (III) quantitative multiparametric analysis including a newly defined gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI). To evaluate the performance of this automated algorithm, the similarity index (k) and the segmentation error including the false-positive rate (r_P ) and the false-negative rate (r_N ) were calculated between the manually defined ground truth and the automatic segmentations of both the ROI and meibomian glands of 15 typical meibography images.

Results

The results of the performance evaluation between the manually defined ground truth and automatic segmentations were as follows: for ROI segmentation, the similarity index (k)=0.94±0.02, the false-positive rate (r_P )=6.02%±2.41%, and the false-negative rate (r_N )=6.43%±1.98%; for meibomian gland segmentation, the similarity index (k)=0.87±0.01, the false-positive rate (r_P )=4.35%±1.50%, and the-false negative rate (r_N )=18.61%±1.54%. The algorithm was successfully applied to process typical meibography images acquired from subjects of different meibomian gland health statuses, by providing the gland area ratio (GA), the gland length (L), gland width (D), gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI).

Conclusions

A fully automated algorithm was developed which demonstrated high similarity with moderate segmentation errors for meibography image segmentation compared with the manual approach, offering multiple parameters to quantify the morphology and function of meibomian glands for the objective evaluation of meibography images.

An Emerging Method to Assess Tear Film Spread and Dynamics as Possible Tear Film Homeostasis Markers

Purpose: This study aims to assess the performance of an analysis method to measure in vivo the movement speed of tear film particles post-blink as a measure of tear film spreading.Materials and methods: Ocular surface parameters and the recording of tear film particles' spreading post-blink were assessed in eighty-one healthy volunteers (43.7 ± 27.0 years) using Keratograph 5 M. The developed software automatically decomposed the video into frames to manually track particles' position for 1.75 seconds after a blink. The following tear film-dynamic metrics were automatically calculated: mean, median, maximum, and minimum particles' speed at different times after blinking and time for particle speed to decrease to <1.20 mm/second. Repeatability of each tear film-dynamic metric and its correlations with ocular surface signs and symptoms were analyzed. Binomial logistic regression was performed to assess the predictability of new metrics to ocular parameters.Results: Repeatability tended to be lower just after blinking (variability of 12.24%), whereas the metrics from 0.5 s onwards had acceptable repeatability (variability below 10%). Tear film-dynamic metrics correlated positively with Non-Invasive Break-Up Time (NIKBUT) while negatively with meibomian gland drop-out. Binomial logistic regression analysis revealed that tear film-dynamic metrics were able to predict NIKBUT. Nevertheless, no statistically significant association was found with gland drop-out. This means that higher particle speed is related to larger NIKBUT. The metric "time for particle speed to decrease to <1.20 mm/second" can be considered the best metric to assess the quality of the tear film, since it was more strongly correlated with NIKBUT (r = 0.42, p = .004), it was more strongly associated in the binomial logistic regression analysis with NIKBUT and showed good repeatability (variability = 5.49%).Conclusions: Tear film-dynamic metrics are emerging homeostasis parameters for assessing indirectly the tear film quality in natural conditions with acceptable repeatability.

Meibomian glands visibility assessment through a new quantitative method

PURPOSE

The aim of this study is to develop a new objective semiautomatic method for analysing Meibomian glands visibility quantitatively.

METHODS

One hundred twelve healthy volunteers aged between 18 and 90 years (48.29 ± 27.46 years) participated in this study. Infrared meibography was obtained from the right upper eyelid through Oculus Keratograph 5 M. Meibographies were classified into 3 groups: Group 1 = patients with good subjective glands visibility and a gland dropout percentage < 1/3 of the total Meibomian gland area; Group 2 = patients with low subjective glands visibility and a gland dropout < 1/3; and Group 3 = patients with low subjective glands visibility and a gland dropout > 1/3. New metrics based on the visibility of the Meibomian glands were calculated and later compared between groups. Rho Spearman test was used to assess the correlation between each metric, and Meibomian gland dropout percentage with the entire sample and after excluding Group 2. A p value less than 0.05 was defined as statistically significant.

RESULTS

Fifty-six subjects were classified in Group 1 (24.48 ± 9.62 years), 19 in Group 2 (69.16 ± 21.30 years) and 37 in Group 3 (73.59 ± 13.70 years). No statistically significant differences were found between Groups 1 and 2 in dropout percentage. All metrics, with the exception of entropy, showed a higher Meibomian gland visibility in Group 1 than in the other two groups. Moderate correlations were statistically significant for all metrics with the exception of entropy. Correlations were higher after excluding Group 2.

CONCLUSION

The proposed method is able to assess Meibomian gland visibility in an objective and repeatable way, which might help clinicians enhance Meibomian gland dysfunction diagnosis and follow-up treatment.

Visible Meibomian Gland Structure Increases After Vectored Thermal Pulsation Treatment in Dry Eye Disease Patients with Meibomian Gland Dysfunction

Purpose

To assess the effect of vectored thermal pulsation treatment (VTP) on visible meibomian gland structure (VGS) in patients with meibomian gland dysfunction (MGD).

Setting

Private group practice (A.T.E.).

Design

Retrospective, single-blinded cohort study.

Methods

Visible meibomian gland structure was evaluated at baseline and at 1-year in treatment (30 patients, 48 eyes) and control (13 patients, 22 eyes) groups. Meibography images were captured using dynamic meibomian imaging. Images were assessed using a novel morphometric analysis technique and analyzed for change in area of VGS (pixels). Additional outcomes measured include tear break up time, corneal staining, tear osmolarity, matrix metalloproteinase-9 (MMP-9), meibography grading, and meibomian gland evaluation.

Results

As high as 69% of eyes in the treatment group showed an improvement in VGS versus 27% of eyes in the control group. As high as 31% of eyes in the treatment group showed a decline in VGS versus 73% of eyes in the control group. TBUT (p = 0.0001), corneal staining (p = 0.0063), and meibomian gland evaluation scores (p = 0.0038) all significantly improved after VTP treatment. However, SPEED scores, MMP-9, tear osmolarity, and meiboscale scores were not significantly improved 1-year post treatment.

Conclusion

A morphometric analysis protocol of meibography provides clinically meaningful information that is undetectable with the standard semiquantitative method of grading meibomian gland structure. This is the first report indicating that gland structure may increase post-VTP relative to untreated controls, thus presenting significant implications regarding benefits and timing of VTP therapy. The described protocol is currently more appropriate for research than for clinical practice.

A review of meibography for a refractive surgeon

Refractive surgery has evolved from being a therapeutic correction of high refractive errors to a cosmetic correction. The expectations associated with such a surgery are enormous and one has to anticipate all possible complications and side-effects that come with the procedure and prepare accordingly. The most common amongst these is post-refractive surgery dry eye of which Meibomian gland dysfunction is a commonly associated cause. We present an understanding of various diagnostic imaging modalities that can be used for evaluating meibomian glands which can also serve as a visual aid for patient understanding. We also describe various common conditions which can silently cause changes in the gland architecture and function which are to be considered and evaluated for.

2D fourier transform for global analysis and classification of meibomian gland images

In recent years, significant progress has been made in the Meibography technique resulting from the use of advanced image analysis methods allowing a quantitative description of the Meibomian gland structures. Many objective measures of gland distortion were previously proposed allowing for user-independent classification of acquired gland images. However, due to the complicated nature of gland deformation, none of the single-valued parameters can fully describe the analyzed gland images. There is a need to increase the number of descriptive factors, selectively sensitive to different gland features. Here we show that global 2D Fourier transform analysis of infra-red gland images provides values of two new such parameters: mean gland frequency and anisotropy in gland periodicity. We show that their values correlate with gland dysfunction and can be used to automatically categorize the images into the three subjective classes (healthy, intermediate and unhealthy). We also demonstrated that classification performance can be improved by dimensionality reduction approach using principal component analysis.

In Vivo Confocal Microscopy Morphometric Analysis of Meibomian Glands in Patients With Graves Ophthalmopathy

PURPOSE

To characterize meibomian glands (MGs) features in patients with Graves ophthalmopathy (GO) by in vivo confocal microscopy (IVCM) and to further investigate possible correlations with ocular surface characteristics.

METHODS

Consecutive patients with GO and controls were enrolled. The following ocular surface parameters were measured: tear break-up time, Schirmer test, and corneal fluorescein staining (Oxford score) were performed on each subject. IVCM of MGs was performed, and the scans were analyzed with ImageJ software for the calculation of the following: acinar unit density, total gland area, total lumen area (TLA), acinar longest diameter, and acinar shortest diameter. A nonparametric Mann-Whitney U test was used to compare variables between patients with GO and controls. The Spearman correlation analysis was used to evaluate the correlations between ocular surface and IVCM parameters.

RESULTS

Twenty-one patients with GO and 24 sex- and age-matched healthy controls were included. Acinar unit density was significantly lower in patients with GO compared with controls (24.5 ± 8.1 vs. 34.2 ± 7.5 U/mm; P < 0.001). In addition, patients with GO showed significantly higher values of TLA, acinar longest diameter, and acinar shortest diameter compared with controls (respectively, 3104.7 ± 1713.3 vs. 1393.8 ± 448.0 μm, 94.4 ± 21.2 vs. 64.3 ± 10.1 µm and 56.6 ± 15.3 vs. 42.2 ± 12.3 μm; always P < 0.05). In patients with GO, TLA showed a significant inverse correlation with Schirmer test (Rs = -0.467; P = 0.038).

CONCLUSIONS

IVCM allowed to detect distinctive features of MGs in patients with GO and could represent a surrogate tool for the assessment of MG status in these patients.