Comparing meibomian gland visibility on optical coherence tomography and Keratograph 5M images using objective and subjective grading methods.

PURPOSE: To investigate if there is a visible difference in meibomian gland (MG) length between images captured with the Visante optical coherence tomography (OCT; wavelength = 1,310 nm) and the OCULUS Keratograph 5M (K5M; wavelength = 880 nm). METHODS: Adults between 18 and 40 years were recruited. Baseline dry eye disease was evaluated with the Standard Patient Evaluation of Eye Dryness (SPEED) and tear meniscus height and tear breakup time with the K5M. Right upper and lower eyelid MGs were imaged with the K5M and Visante OCT. Each image was graded with the 0 to 3 meiboscore scale. The central 5 MGs were evaluated with ImageJ for percent gland length visibility. RESULTS: Thirty participants were analyzed with a median (interquartile range [IQR]) age of 23.0 (5.0) years (53.3 % female). Overall, participants were asymptomatic and had normal tear films. Meiboscores based on K5M and Visante OCT was significantly different for the lower eyelid (0[1] vs 1[2]; p = 0.007) but not the upper eyelid (0[1] vs 0[1]; p = 1.00). The mean percent gland visibility of the upper eyelid (82.7[9.6] vs 75.2[13.5]; p < 0.001) and the lower eyelid (81.2[12.7] vs 64.1[17.6]; p < 0.001) were significantly greater on the Visante OCT than the K5M images, respectively. CONCLUSION: OCT images had significantly greater percent visible MG lengths than the K5M images. This suggests viable segments of the MGs may be missed with typical imaging, which may explain how it is possible that studies have found less post-treatment MG atrophy.

Noninvasive Imaging of Conjunctival Goblet Cells as a Method for Diagnosing Dry Eye Disease in an Experimental Mouse Model.

Purpose: The purpose of this study was to evaluate a noninvasive conjunctival goblet cell (GC) imaging method for assessing dry eye disease (DED) in an experimental mouse model. Methods: Moxifloxacin-based fluorescence microscopy (MBFM) was used to examine GCs noninvasively in 56 mice. Forty-two (42) DED-induced mice were divided into 2 groups and treated topically for 14 days with cyclosporine (CsA) or normal saline (NS). In vivo MBFM imaging and clinical DED evaluations were performed and goblet cell density (GCD) and goblet cell area (GCA) were obtained and compared with histological GCD using periodic acid-Schiff (PAS) staining. Correlation and receiver operating characteristic (ROC) analyses showed MBFM's high diagnostic value. Results: The GCD and GCA of the DED mice obtained from in vivo MBFM imaging were highly correlated with clinical DED parameters and GCD obtained from PAS histology. The therapeutic effect of CsA, as observed by in vivo MBFM, was significant with respect to that of NS treatment. The ROC curves derived from in vivo MBFM showed high diagnostic value in assessing DED. Conclusions: The proposed noninvasive method has high diagnostic value in assessing the severity of DED and the effect of treatment for this disease. Translational Relevance: A noninvasive imaging method using moxifloxacin-based fluorescence microscopy was evaluated for assessing DED in an experimental mouse model. The method showed high diagnostic value in assessing the severity of DED and the effect of treatment, bridging the gap between basic research and clinical treatment. The study provides a promising tool for diagnosing and monitoring DED.

Unsupervised Learning Based on Meibography Enables Subtyping of Dry Eye Disease and Reveals Ocular Surface Features.

Purpose: This study aimed to establish an image-based classification that can reveal the clinical characteristics of patients with dry eye using unsupervised learning methods. Methods: In this study, we analyzed 82,236 meibography images from 20,559 subjects. Using the SimCLR neural network, the images were categorized. Data for each patient were averaged and subjected to mini-batch k-means clustering, and validated through consensus clustering. Statistical metrics determined optimal category numbers. Using a UNet model, images were segmented to identify meibomian gland (MG) areas. Clinical features were assessed, including tear breakup time (BUT), tear meniscus height (TMH), and gland atrophy. A thorough ocular surface evaluation was conducted on 280 cooperative patients. Results: SimCLR neural network achieved clustering patients with dry eye into six image-based subtypes. Patients in different subtypes harbored significantly different noninvasive BUT, significantly correlated with TMH. Subtypes 1 and 5 had the most severe MG atrophy. Subtype 2 had the highest corneal fluorescent staining (CFS). Subtype 4 had the lowest TMH, whereas subtype 5 had the highest. Subtypes 3 and 6 had the largest MG areas, and the upper MG areas of a person's bilateral eyes were highly correlated. Image-based subtypes are related to meibum quality, CFS, and morphological characteristics of MG. Conclusions: In this study, we developed an unsupervised neural network model to cluster patients with dry eye into image-based subtypes using meibography images. We annotated these subtypes with functional and morphological clinical characteristics.

Assessment of corneal sublayer thickness changes in glaucoma patients using optical coherence tomography and correlation of epithelial layer thinning with dry eye monitoring.

BACKGROUND: In this study, we aimed to assess the central corneal epithelial thickness (CET), central corneal stromal thickness (CST), and total central corneal thickness (CCT) thinning relationships with dry eye development monitoring and underestimated measurement of intraocular pressure (IOP) in primary open-angle glaucoma (POAG) patients treated with timolol, dorzolamide, and brimonidine. METHODS: This longitudinal cohort study included 106 patients with POAG. All patients underwent a detailed ophthalmic examination. In addition, CET, CST, and CCT were measured using anterior segment optical coherence tomography (AS-OCT). Subsequently, the cohort was divided into three groups based on the therapy administered. The Tomec group received monotherapy with benzalkonium chloride (BAK)-preserved timolol + dorzolamide fixed combination. The Alphagan group received monotherapy with purite-preserved brimonidine, and the Combigan group received monotherapy with BAK-preserved timolol + brimonidine fixed combination. RESULTS: CET, CST, and CCT did not show a statistically significant decrease in the Alphagan group (p>0.05). However, the Tomec and Combigan groups showed significantly reduced measurements, except for stromal thickness (p<0.05). Finally, a significant positive correlation was found between changes in tear break-up time (TBUT) and CET during the follow-up period (r = 0.637, p = 0.001). CONCLUSIONS: CET and CCT thinning were higher in the Tomec and Combigan groups than in the Alphagan group. Furthermore, although CCT reduction was significant in the Tomec and Combigan groups, its effect on IOP underestimation was approximately 1%. Furthermore, the positive correlation between CET and TBUT suggests that CET measurement with AS-OCT may also be useful in dry eye monitoring.

Validation of a Semiautomatic Optical Coherence Tomography Digital Image Processing Algorithm for Estimating the Tear Meniscus Height.

Purpose: To design and validate a high-sensitivity semiautomated algorithm, based on adaptive contrast image, able to identify and quantify tear meniscus height (TMH) from optical coherence tomography (OCT) images by using digital image processing (DIP) techniques. Methods: OCT images of the lacrimal meniscus of healthy patients and with dry eye are analyzed by our algorithm, which is composed of two stages: (1) the region of interest and (2) TMH detection and measurement. The algorithm performs an adaptive contrast sequence based on morphologic operations and derivative image intensities. Trueness, repeatability, and reproducibility for TMH measurements are computed and the algorithm performance is statistically compared against the corresponding negative obtained manually by using a commercial software. Results: The algorithm showed excellent repeatability supported by an intraclass correlation coefficient equal to 0.993, a within-subject standard deviation equal to 9.88, and a coefficient of variation equal to 2.96%, and for the reproducibility test, the results did not show a significant difference as the mean value was 244.4 ± 114.9 µm for an expert observer versus 242.4 ± 111.2 µm for the inexperienced observer (P = 0.999). The method strongly suggests the algorithm can predict measurements that are manually performed with commercial software. Conclusions: The presented algorithm possess high potential to identify and measure TMH from OCT images in a reproducible and repeatable way with minimal dependency on user. Translational Relevance: The presented work shows a methodology on how, by using DIP, it is possible to process OCT images to calculate TMH and aid ophthalmologists in the diagnosis of dry eye disease.

Non-invasive and objective tear film breakup detection on interference color images using convolutional neural networks.

PURPOSE: Dry eye disease affects hundreds of millions of people worldwide and is one of the most common causes for visits to eye care practitioners. The fluorescein tear breakup time test is currently widely used to diagnose dry eye disease, but it is an invasive and subjective method, thus resulting in variability in diagnostic results. This study aimed to develop an objective method to detect tear breakup using the convolutional neural networks on the tear film images taken by the non-invasive device KOWA DR-1α. METHODS: The image classification models for detecting characteristics of tear film images were constructed using transfer learning of the pre-trained ResNet50 model. The models were trained using a total of 9,089 image patches extracted from video data of 350 eyes of 178 subjects taken by the KOWA DR-1α. The trained models were evaluated based on the classification results for each class and overall accuracy of the test data in the six-fold cross validation. The performance of the tear breakup detection method using the models was evaluated by calculating the area under curve (AUC) of receiver operating characteristic, sensitivity, and specificity using the detection results of 13,471 frame images with breakup presence/absence labels. RESULTS: The performance of the trained models was 92.3%, 83.4%, and 95.2% for accuracy, sensitivity, and specificity, respectively in classifying the test data into the tear breakup or non-breakup group. Our method using the trained models achieved an AUC of 0.898, a sensitivity of 84.3%, and a specificity of 83.3% in detecting tear breakup for a frame image. CONCLUSIONS: We were able to develop a method to detect tear breakup on images taken by the KOWA DR-1α. This method could be applied to the clinical use of non-invasive and objective tear breakup time test.

The lacrimal gland in Sjögren's syndrome: can we unravel its mystery using ultrasound?

According to a recent survey of patients with the autoimmune disease primary Sjögren's syndrome (pSS), dry eye symptoms are present in 95-98% of pSS patients. As one of the most disabling symptoms mentioned by pSS patients, dry eyes have demonstrable effects on quality of their life, leading to eye dryness, itching, and pain, with some patients describing as a recurrent sensation of sand or gravel in the eyes. The symptoms are matched only in prevalence by dry mouth and chronic fatigue. In contrast to the prevalence of dry eye symptoms in pSS and their burden on pSS patients, our comprehension of dry eye disease development is minimal; specifically how function of the tear-fluid producing gland the lacrimal gland (LG), manifests. The comparison becomes stronger again when we consider what we know about dysfunction of the salivary gland (SG) in pSS, for example the appreciation of the transcriptome of 'innately activated' B cells invading the SG, their complicity in formation of lymphoepithelial lesions, and the ability of the SG epithelium to actively contribute to the inflammatory milieu. The exploration of ultrasound imaging as an additional modality to garner information about SG dysfunction in pSS has opened many doors for non-invasive, repeatable imaging in pSS. Here we summarise SG histology and ultrasound phenotype briefly and then juxtapose this with available studies examining LG pathology and ultrasound, and our understanding of LG dysfunction in pSS.

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.

Effects of Medium-Term Soft Contact Lens Fitting on Dry Eye: Analyses Using Ultra-High Resolution Optical Coherence Tomography and Digital Slit-Lamp Biomicroscopy.

Purpose: This study aimed to evaluate the medium-term fit of soft contact lenses (SCLs) and its effects on dry eye by using ultra-high-resolution optical coherence tomography (UHR-OCT) and digital slit-lamp biomicroscopy (DSLB) and identify factors indicating a better contact lens fit. Methods: A total of 14 participants (7 men, 7 women; mean age, 23.1 years) were recruited. Temporal lag and conjunctival coverage were imaged by UHR-OCT. DSLB was used to assess lens decentration and blink-induced movement. In addition, dry eye indices, including ocular surface disease index (OSDI), tear break-up time (TBUT), and Schirmer I test scores, were measured at baseline and 1 day, 1 week, 1 month, 3 months, and 6 months after wearing. Results: Good repeatability was found in the assessments of lens fit characteristics by two researchers and in the assessments by one researcher at different follow-up times. There were no significant differences in lens fit characteristics, TBUT and Schirmer I test scores at each checkpoint (P >0.05). However, the OSDI was significantly higher than baseline after wearing (P <0.05). Changes in the temporal lag were positively correlated with changes in TBUT (r =0.544, P =0.044) and negatively correlated with changes in OSDI (r = -0.629, P =0.016). Changes in blink-induced movement were positively correlated with changes in OSDI, Schirmer I test scores, and TBUT (P <0.05). Conclusions: UHR-OCT and DSLB can be used to comprehensively and quantitatively evaluate SCL fit. Both the contact lenses and ocular surface conditions are almost stable within 6 months of SCL wear. Temporal lag and blink-induced movement have a strong correlation with dry eye parameters in SCL wearers.

Altered regional brain white matter in dry eye patients: a brain imaging study.

This study aimed to investigate the regional changes of brain white matter (WM) in DE patients using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). A total of 25 dry eye patients (PAT) and 25 healthy controls (HC) were recruited. All subjects underwent DTI and NODDI, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), isotropic volume fraction (FISO), intra-cellular volume fraction (FICVF), and orientation dispersion index (ODI) were obtained respectively. Then complete Hospital Anxiety and Depression Scale (HADS), anxiety score (AS) or depression scores (DS) were obtained. Receiver operating characteristic (ROC) curve analysis was used to evaluate the reliability of DTI and NODDI in distinguishing the two groups. DTI revealed that PAT had lower FA in both the left superior longitudinal fasciculus (LSLF) and the corpus callosum (CC), and higher MD in the LSLF, the right posterior limb of the internal capsule and the right posterior thalamic radiation. PAT had significant AD changes in regions including the genu of the CC, the right posterior limb of internal capsule, and the right splenium of the CC. From NODDI, PAT showed increased ODI in the LSLF and increased FISO in the right splenium of the CC. FICVF showed a significant decrease in the LSLF while increased in the left anterior corona radiata and the CC. Furthermore, the average values of MD and FICVF were significantly correlated with DS and AS. Hence the results of this study suggest that there are regional changes in WM in DE patients which may contribute to further understanding of the pathological mechanism of DE.

Random Forest Algorithm-Based Ultrasonic Image in the Diagnosis of Patients with Dry Eye Syndrome and Its Relationship with Tear Osmotic Pressure.

The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were (27.8 ± 30.6) µm and (29.1 ± 30.9) µm, respectively. 95% confidence interval was 22.7-34.2 µm. In patients with moderate dry eye, the average CCT measured in group A was (-6.31 ± 2.82) µm, and that in group B was (-6.45 ± 3.06) µm. The 95% confidence interval of the difference between the two is -7.66~-5.43 µm. In patients with severe dry eye, the average CCT was (-3.78 ± 1.13) µm in group A and (-7.09 ± 2.05) µm in group B (P < 0.05). The 95% confidence interval of the difference between the two is -7.05~ -5.11 µm. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe (P < 0.05). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome.

Investigation of changes in the activity and function of dry eye-associated brain regions using the amplitude of low-frequency fluctuations method.

OBJECTIVE: The local characteristics of spontaneous brain activity in patients with dry eye (DE) and its relationship with clinical characteristics were evaluated using the amplitude of low-frequency fluctuations (ALFF) method. METHODS: A total of 27 patients with DE (10 males and 17 females) and 28 healthy controls (HCs) (10 males and 18 females) were recruited, matched according to sex, age, weight and height, classified into the DE and HC groups, and examined using functional magnetic resonance imaging (fMRI) scans. Spontaneous brain activity changes were recorded using ALFF technology. Data were recorded and plotted on the receiver operating characteristic (ROC) curve, reflecting changes in activity in different brain areas. Finally, Pearson correlation analysis was used to calculate the potential relationship between spontaneous brain activity abnormalities in multiple brain regions and clinical features in patients with DE. GraphPad Prism 8 (GraphPad Software, Inc.) was used to analyze the linear correlation between the Hospital Anxiety and Depression Scale and ALFF value. RESULTS: Compared with HCs, the ALFF values of patients with DE were decreased in the right middle frontal gyrus (MFG)/right inferior orbitofrontal cortex (OFC), left triangle inferior frontal gyrus, left MFG, and right superior frontal gyrus. In contrast, the ALFF value of patients with DE was increased in the left calcarine. CONCLUSION: There are significant fluctuations in the ALFF value of specific brain regions in patients with DE versus HCs. This corroborates previous evidence showing that the symptoms of ocular surface damage in patients with DE are related to dysfunction in specific brain areas.

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.

Changes in anterior segment parameters and presence of dry eye disease in patients with acromegaly: A Sirius topography study combined with meibography.

PURPOSE: To investigate anterior segment parameters (ASPs) and dry eye disease (DED), including the status of the meibomian glands, in patients with acromegaly. METHODS: In this cross-sectional, comparative study, 36 acromegaly patients and 40 healthy sex- and age-matched controls were included. Participants received a comprehensive ophthalmological examination, including intraocular pressure measurements with Goldmann applanation tonometry (IOPGAT) and central corneal thickness corrected intraocular pressure (IOPCCT) measurements, and were evaluated for ASPs and DED. For ASPs, white-to-white (WTW), apical (ACT) and thinnest corneal thickness (TCT), corneal volume (CV), keratometry readings (K1, K2, and Kmean), anterior chamber depth (ACD) and volume (ACV), and iridocorneal angle (ICA) were obtained via Sirius topography. DED was assessed with Schirmer's test, tear breakup time (TBUT), and Ocular Surface Disease Index (OSDI) scores. Meibography scores (MSs) were obtained with the Sirius topography device. RESULTS: Patients had higher mean IOPGAT (P = .006), IOPCCT (P = .01), ACT (P = .024), and TCT (P = .005) but narrower ICA (P = .014) than controls. Although Schirmer's test did not differ between the groups (P = .442), patients had higher OSDI (P < .001), higher MS (P = .001), and shorter TBUT (P = .002). CONCLUSION: Patients with acromegaly have greater IOP, greater corneal thickness, but narrower ICA than healthy individuals, as well as DED with increased MSs, which suggests meibomian gland dysfunction.

A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images.

PURPOSE: Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in computer assisted diagnosis. This study aimed to develop deep transfer learning models for automatic detection of activated dendritic cells and inflammatory cells using in vivo confocal microscopy images. METHODS: A total of 3453 images was used to train the models. External validation was performed on an independent test set of 558 images. A ground-truth label was assigned to each image by a panel of cornea specialists. We constructed a deep transfer learning network that consisted of a pre-trained network and an adaptation layer. In this work, five pre-trained networks were considered, namely VGG-16, ResNet-101, Inception V3, Xception, and Inception-ResNet V2. The performance of each transfer network was evaluated by calculating the area under the curve (AUC) of receiver operating characteristic, accuracy, sensitivity, specificity, and G mean. RESULTS: The best performance was achieved by Inception-ResNet V2 transfer model. In the validation set, the best transfer system achieved an AUC of 0.9646 (P<0.001) in identifying activated dendritic cells (accuracy, 0.9319; sensitivity, 0.8171; specificity, 0.9517; and G mean, 0.8872), and 0.9901 (P<0.001) in identifying inflammatory cells (accuracy, 0.9767; sensitivity, 0.9174; specificity, 0.9931; and G mean, 0.9545). CONCLUSIONS: The deep transfer learning models provide a completely automated analysis of corneal inflammatory cellular components with high accuracy. The implementation of such models would greatly benefit the management of corneal diseases and reduce workloads for ophthalmologists.

Association Between Dry Eye Syndrome and Osteoarthritis Severity: A Nationwide Cross-Sectional Study (KNHANES V).

OBJECTIVE: Recently, several researchers reported an association between osteoarthritis and dry eye syndrome (DES) and suggested that they may be related to central sensitization. We investigated the association between DES, osteoarthritis pain, and radiographic severity. DESIGN: A nationwide, cross-sectional study. SETTING: The Fifth Korean National Health and Nutrition Examination Survey. SUBJECTS: 8,664 participants in this survey. METHODS: Osteoarthritis-associated radiographic changes were evaluated by trained radiologists. Multivariable logistic regression was performed to examine the relationship between osteoarthritis pain, radiographic severity, and DES. The odds ratios (ORs) of DES were analyzed in accordance with the presence of osteoarthritis-associated factors. RESULTS: Radiographic knee osteoarthritis was significantly associated with lower ORs for DES (OR = 0.80, P = 0.046, for diagnosed/self-reported DES; OR = 0.84, P = 0.034, for symptoms of DES). Knee pain or stiffness was significantly associated with higher ORs for DES (OR = 1.28, P = 0.020, for diagnosed/self-reported DES; OR = 1.29, P = 0.003, for symptoms of DES). In patients with symptomatic osteoarthritis, DES was not significantly associated with radiographic severity of osteoarthritis. In patients with nonsymptomatic radiographic osteoarthritis, DES was inversely related with radiographic severity (Ptrend = 0.012 for diagnosed/self-reported DES; Ptrend < 0.001 for symptoms of DES). CONCLUSIONS: Radiographic osteoarthritis showed a significant association with decreased DES. Patients with less pain and severe radiographic OA were more likely to have less DES. Our results suggest that the degree of pain caused by osteoarthritis and central sensitization may be closely associated with DES.

The Impact of Artificial Tear Viscosity on the Results of Optical Coherence Tomography. / Einfluss der Tränenersatzmittel-Viskosität auf die Messergebnisse der optischen Kohärenztomografie.

BACKGROUND: In earlier studies, an irregular tear film could be responsible for artifacts in imaging procedures in ophthalmology. As a result, this study will investigate the influence of hyaluronic acid-containing tear substitutes of different viscosities on the measurement results of optical coherence tomography and on the non-invasive tear film break-up time. METHODS: The study included three sessions in which three hyaluronic acid tear substitutes of different viscosities (Hylo-Vision 0.1%, 0.2%, 0.3%, OmniVision GmbH, Puchheim, Germany) were applied to each subject. In 20 healthy volunteers, the non-invasive tear film break-up time was measured before and after the application of the tear substitutes using Keratograph 5M (Oculus GmbH, Wetzlar, Germany) and optical coherence tomography using 3D OCT-2000 (Topcon, Hamburg, Germany). RESULTS: The median age of the volunteers was 28.5 years. 11 women (55%) and 9 men (45%) were examined. The non-invasive tear film break-up time improved significantly (p = 0.027) after the application of 0.3% hyaluronic acid. In the study, no significant difference was found in all OCT parameters used before and after application of the different viscous hyaluronic acid-containing tear substitutes. CONCLUSION: In this study, no influence of tear substitutes containing different viscosities of hyaluronic acid on the measurement results of optical coherence tomography could be determined. Therefore, 0.1%, 0.2% and 0.3% hyaluronic acid can be applied to the patient to improve the corneal surface before the examination with optical coherence tomography, without influencing the measurement results of optical coherence tomography.

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.

New Freeware for Image Analysis of Lissamine Green Conjunctival Staining.

PURPOSE: Lissamine green (LG) is often used in addition to fluorescein to assess the severity of conjunctival damage in dry eye syndrome, which is graded manually. Our purpose was to describe an algorithm designed for image analysis of LG conjunctival staining. METHODS: Twenty pictures of patients suffering from dry eye with visible LG conjunctival staining were selected. The images were taken by 2 different digital slit lamps with a white light source and a red filter transmitting over the wavelengths absorbed by LG. Conjunctival staining appeared in black on a red background. The red channel was extracted from the original image. Stained areas were then detected using a Laplacian of Gaussian filter and applying a threshold whose value was determined manually on a subset of images. The same algorithm parameters remained constant thereafter. LG-stained areas were also drawn manually by 2 experts as a reference. RESULTS: The delineation obtained by the algorithm closely matched the actual contours of the punctate dots. In 19 cases of 20 (95%), the algorithm found the same Oxford grade as the experts, even for confluent staining that was detected as a multitude of dots by the algorithm but not by the experts, resulting in a high overestimation of the total number of dots (without mismatching the Oxford grade estimated by the experts). The results were similar for the 2 slit-lamp imaging systems. CONCLUSIONS: This efficient new image-analysis algorithm yields results consistent with subjective grading and may offer advantages of automation and scalability in clinical trials.