A quantitative method for assessing the quality of meibomian glands

Global research of artificial intelligence in eyelid diseases: A bibliometric analysis

Purpose

To generate an overview of global research on artificial intelligence (AI) in eyelid diseases using a bibliometric approach.

Methods

All publications related to AI in eyelid diseases from 1900 to 2023 were retrieved from the Web of Science (WoS) Core Collection database. After manual screening, 98 publications published between 2000 and 2023 were finally included. We analyzed the annual trend of publication and citation count, productivity and co-authorship of countries/territories and institutions, research domain, source journal, co-occurrence and evolution of the keywords and co-citation and clustering of the references, using the analytic tool of the WoS, VOSviewer, Wordcloud Python package and CiteSpace.

Results

By analyzing a total of 98 relevant publications, we detected that this field had continuously developed over the past two decades and had entered a phase of rapid development in the last three years. Among these countries/territories and institutions contributing to this field, China was the most productive country and had the most institutions with high productivity, while USA was the most active in collaborating with others. The most popular research domains was Ophthalmology and the most productive journals were Ocular Surface. The co-occurrence network of keywords could be classified into 3 clusters respectively concerned about blepharoptosis, meibomian gland dysfunction and blepharospasm. The evolution of research hotspots is from clinical features to clinical scenarios and from image processing to deep learning. In the clustering analysis of co-cited reference network, cluster "0# deep learning" was the largest and latest, and cluster "#5 meibomian glands visibility assessment" existed for the longest time.

Conclusions

Although the research of AI in eyelid diseases has rapidly developed in the last three years, there are still gaps in this area. Our findings provide researchers with a better understanding of the development of the field and a reference for future research directions.

Advances in artificial intelligence for meibomian gland evaluation: A comprehensive review

Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Conventional manual evaluation techniques for MGD face limitations characterized by inefficiencies, high subjectivity, limited big data processing capabilities, and a dearth of quantitative analytical tools. With rapidly advancing artificial intelligence (AI) techniques revolutionizing ophthalmology, studies are now leveraging sophisticated AI methodologies--including computer vision, unsupervised learning, and supervised learning--to facilitate comprehensive analyses of meibomian gland (MG) evaluations. These evaluations employ various techniques, including slit lamp examination, infrared imaging, confocal microscopy, and optical coherence tomography. This paradigm shift promises enhanced accuracy and consistency in disease evaluation and severity classification. While AI has achieved preliminary strides in meibomian gland evaluation, ongoing advancements in system development and clinical validation are imperative. We review the evolution of MG evaluation, juxtapose AI-driven methods with traditional approaches, elucidate the specific roles of diverse AI technologies, and explore their practical applications using various evaluation techniques. Moreover, we delve into critical considerations for the clinical deployment of AI technologies and envisages future prospects, providing novel insights into MG evaluation and fostering technological and clinical progress in this arena.

Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review

In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.

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.

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.

Advances in artificial intelligence applications for ocular surface diseases diagnosis

In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

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.

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.

Spontaneous acinar and ductal regrowth after meibomian gland atrophy induced by deletion of FGFR2 in a mouse model

PURPOSE

We have demonstrated that deletion of fibroblast growth factor receptor 2 gene (Fgfr2) leads to Meibomian gland (MG) atrophy in an inducible conditional knockout mouse model, referred as Fgfr2^CKO. Herein, we investigated whether MG spontaneously recovers after atrophy in this model.

METHODS

Two months old Fgfr2^CKO mice were injected peritoneally once or twice of doxycycline (Dox) at 80 μg/gm of body weight to induce MG atrophy of various severities via Fgfr2 deletion. Recovery of acinar and ductal tissues was monitored by meibography, lipid staining and immunofluorescence against keratin-6a in MG whole-mount. Biomarkers for acinar and ductal differentiation and proliferation were also examined by immunostaining.

RESULTS

Single Dox injection in Fgfr2^CKO mice caused severe acinar and moderate ductal atrophy. Severe ductal shortening or loss occurred after second Dox injection, presumably related to the reported slower cycling of the ductal epithelia. Spontaneous acinar regrowth after atrophy was observed over a period of 60 days in both injection regimens. However, less robust acinar recovery was associated with more disrupted ductal structures in twice injected Fgfr2^CKO mice.

CONCLUSIONS

Our current findings further substantiate the role of FGFR2 in MG homeostasis, and suggest that FGFR2-signaling may provide a potential strategy for regenerating acini from age-related MG dysfunction in humans. Our data demonstrated that spontaneous MG recovery depends on the extent of ductal atrophy, suggesting that ductal epithelia may provide the progenitor cells for acinar regeneration. Nonetheless, the role of ductal tissue as the source of acinar progenitors awaits further investigation.

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.

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.

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.

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.

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.

The role of meibography in ocular surface diagnostics: A review

The meibomian glands are lipid-secreting glands located in the tarsal plates, whose secretory products cover the tear film, thereby reducing evaporation as well as ensuring lubrication of the ocular surface. The meibomian glands can be visualized at different levels of magnification by infrared meibography, laser confocal microscopy, and optical coherence tomography. These imaging modalities have been subject to much research and progress in clinical practice and have shaped our current understanding of meibomian glands in health and disease. In this review, we explore the evolution of meibography over the past decades, the major contributions of various meibographic modalities, and discuss their clinical significance.

Meibomian Gland Morphology: The Influence of Structural Variations on Gland Function and Ocular Surface Parameters

PURPOSE

To objectively and quantitatively characterize meibomian gland morphology and to investigate the influence of morphological variations on gland function and ocular surface and tear film parameters.

METHODS

One hundred fifty subjects were enrolled. The examinations included tear osmolarity, tear meniscus height, bulbar conjunctival hyperemia, noninvasive tear film breakup time, lid margin thickness, foam secretion, meibomian gland expressibility, count of functioning glands, corneal and conjunctival staining, fluorescein breakup time, lid wiper epitheliopathy, and Schirmer test. Patient symptoms were assessed using the Ocular Surface Disease Index questionnaire. Images from noncontact meibography were analyzed using an automated method that objectively estimates dropout area, number of glands, gland length and width, and gland irregularity.

RESULTS

Gland irregularity highly correlated with dropout area (r = -0.4, P < 0.001) and showed significant partial correlations with fluorescein breakup time (r = 0.162, P = 0.049) and the Ocular Surface Disease Index questionnaire (r = -0.250, P = 0.002) Subjects with dropout area <32% were divided into 2 groups: high and low irregularity. Gland expressibility was statistically significantly different between the 2 groups (U = 319.5, P = 0.006). In the high irregularity group, gland irregularity correlated with the Schirmer test (r = 0.530, P = 0.001) and corneal fluorescein staining (r = -0.377, P = 0.021).

CONCLUSIONS

Automated morphological analysis of meibomian gland structure provides additional quantitative and objective information regarding gland morphology. The link between dropout area and gland function is not clear. Assessment of gland irregularity might better predict gland function and its effects on ocular surface and tear film parameters.

Infrared meibography allows detection of dimensional changes in meibomian glands following intranasal neurostimulation

PURPOSE

Patients with dry eye disease (DED) may suffer from decreased tear break-up time due to meibomian gland (MG) dysfunction. Infrared meibography (IR Meibography) uses infrared wavelength light to visualize meibomian glands in vivo. We aimed to explore the feasibility of using serial IR Meibography imaging to assess morphological changes in MGs as an indirect measure of functionality, following intranasal neurostimulation (ITN).

METHODS

Fifteen DED subjects were prospectively enrolled in a single-center, single-arm study. Changes in MGs were captured using IR meibography (RTVUE-XR, Optovue, Inc. Fremont, CA, USA) on the lower eyelids before and after 3 min of ITN (TrueTear®, Allergan, Dublin, Ireland) use that delivers a microcurrent to sensory neurons of the nasal cavity. The same MGs were selected pre- and post-stimulation, and MG area and perimeter were analyzed by two masked observers.

RESULTS

Mean (±SD) pre- and post-stimulation MG areas were 2,187.60 ± 635.88 μm² and 1,933.20 ± 538.55 μm², respectively. The mean change in area, 254.49 μm², representing an 11.6% reduction following ITN use, was statistically significant (p = 0.001). Mean (±SD) pre- and post-stimulation MG perimeters were 235.9 ± 51.38 μm and 222.2 ± 47.72 μm, respectively. The mean change in perimeter, 13.7 μm, representing a 5.81% reduction following ITN use, was statistically significant (p = 0.012).

CONCLUSIONS

Our study shows that IR meibography can be used to detect immediate changes in gland area and perimeter, an indirect measure of MG activity following intervention by ITN.

Ocular surface analysis and automatic non-invasive assessment of tear film breakup location, extension and progression in patients with glaucoma

Background

Tear film stability is the key event in ocular surface diseases. The purpose of this study is to evaluate spatial and temporal progression of the tear film breakup using an automatic non-invasive device.

Methods

Non-invasive tear breakup time (NITBUT) parameters, such as First NITBUT (F-NITBUT) and Average NITBUT (A-NITBUT), were evaluated in 132 glaucoma and 87 control eyes with the Keratograph 5 M device. Further analysis of this data was used to determine size, location and progression of tear film breakup with automatically identified breakup areas (BUA). The progression from First BUA (F-BUA) to total BUA (T-BUA) was expressed as Dry Area Growth Rate (DAGR). Differences between both groups were analysed using Student t-test for parametric data and Mann-Whitney U test for non-parametric data. Pearson’s correlation coefficient was used to assess the relationship between parametric variables and Spearman in the case of non-parametric variables.

Results

F-NITBUT was 11.43 ± 7.83 s in the control group and 8.17 ± 5.73 in the glaucoma group (P = 0.010). A-NITBUT was 14.04 ± 7.21 and 11.82 ± 6.09 s in control and glaucoma groups, respectively (P = 0.028). F-BUA was higher in the glaucoma group than in the control group (2.73 and 2.28; P = 0.022) and was more frequently located at the centre of the cornea in the glaucoma group (P = 0.039). T-BUA was also higher in the glaucoma group than in the control group (13.24 and 9.76%; P = 0.012) and the DAGR was steeper in the glaucoma group than in the control group (34.38° and 27.15°; P = 0.009).

Conclusions

Shorter NITBUT values and bigger, more central tear film breakup locations were observed in the glaucoma group than in the control group. The DAGR indicates that tear film rupture is bigger and increases faster in glaucomatous eyes than in normal eyes.

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

Purpose

We present and validate a new methodology for analyzing, in an automated and objective fashion, infrared images of the meibomian glands (MG).

Methods

The developed algorithm consists of three main steps: selection of the region of interest, detection of MG, and analysis of MG morphometric parameters and dropout area (DOA). Additionally, a new approach to quantify the irregularity of MG is introduced. We recruited 149 adults from a general population. Infrared meibography, using Keratograph 5M, was performed. Images were assessed and graded subjectively (Meiboscore) by two experienced clinicians and objectively with the proposed automated method.

Results

The correlation of subjective DOA assessment between the two clinicians was poor and the average percentage of DOA estimated objectively for each Meiboscore group did not lie within their limits. The objective assessment showed lower variability of meibography grading than that obtained subjectively. Additionally, a new grading scale of MG DOA that reduces intraclass variation is proposed. Reported values of MG length and width were inversely proportional to the DOA. Gland irregularity was objectively quantified.

Conclusions

The proposed automatic and objective method provides accurate estimates of the DOA as well as additional morphologic parameters that could add valuable information in MG dysfunction understanding and diagnosis.

Translational Relevance

This approach highlights the shortcomings of currently used subjective methods, and provides the clinicians with an objective, quantitative and less variable alternative for assessing MG in a noninvasive and automated fashion. It provides a viable alternative to more time-consuming subjective methods.

A clinical utility assessment of the automatic measurement method of the quality of Meibomian glands

Background

Meibomian gland dysfunction (MGD) is one of the most common diseases observed in clinics and is the leading cause of evaporative dry eye. Today, diagnostics of MGD is not fully automatic yet and is based on a qualitative assessment made by an ophthalmologist. Therefore, an automatic analysis method was developed to assess MGD quantiatively.

Materials

The analysis made use of 228 images of 57 patients recorded by OCULUS Keratograph^® 5 M with a resolution of 1024 × 1360 pixels concern 30 eyes of healthy individuals (14 women and 16 men) and 27 eyes of sick patients (10 women and 17 men). The diagnosis of dry eye was made according to the consensus of DED in China (2013).

Methods

The presented method of analysis is a new, developed method enabling an automatic, reproducible and quantitative assessment of Meibomian glands. The analysis relates to employing the methods of analysis and image processing. The analysis was conducted in the Matlab environment Version 7.11.0.584, R2010b, Java VM Version: Java 1.6.0_17-b04 with Sun Microsystems Inc. with toolboxes: Statistical, Signal Processing and Image Processing.

Results

The presented, new method of analysis of Meibomian glands is fully automatic, does not require operator’s intervention, allows obtaining reproducible results and enables a quantitative assessment of Meibomian glands. Compared to the other known methods, particularly with the method described in literature it allows obtaining better sensitivity (98%) and specificity (100%) results by 2%.