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

Meibomian gland alterations in allergic conjunctivitis: insights from a novel quantitative analysis algorithm

Purpose

To investigate the changes in meibomian gland (MG) structure in allergic conjunctivitis (AC) patients using an intelligent quantitative analysis algorithm and to explore the relationship between these changes and clinical parameters.

Methods

A total of 252 eyes from patients with AC and 200 eyes from normal controls were examined. Infrared meibography was performed using the non-contact mode of the Keratograph 5M. MG images were analyzed using a deep learning-based a quantitative analysis algorithm to evaluate gland length, area, dropout ratio, and deformation. Clinical parameters, including tear meniscus height, tear break up time (TBUT), conjunctival hyperemia, and Ocular Surface Disease Index (OSDI) scores, were assessed and correlated with changes in the structure of MG.

Results

The average MG length in AC patients was 4.48 ± 1.04 mm, shorter compared to the control group (4.72 ± 0.94 mm). The average length of the central 5 glands in AC patients was 4.94 ± 1.67 mm, which was also shorter than the control group's central 5 glands (5.38 ± 1.42 mm). Furthermore, the central 5 glands' area in AC patients (1.61 ± 0.64 mm2) was reduced compared to the control group (1.79 ± 0.62 mm2). Tear meniscus height was lower in the allergy group (0.26 ± 0.10 mm) compared to the control group (0.44 ± 0.08 mm) (P < 0.05). The non-invasive first tear film break-up time was shorter in the allergy group (8.65 ± 6.31 s) than in the control group (10.48 ± 2.58 s) (P < 0.05). Conjunctival congestion was higher in the allergy group (1.1 ± 0.52) compared to the control group (0.97 ± 0.30) (P < 0.05). The OSDI score in the allergy group (8.33 ± 7.6) was higher than that in the control group (4.00 ± 0.50) (P < 0.05). Correlation analysis revealed that the gland dropout ratio was positively associated with male gender and negatively associated with age and OSDI scores. Additionally, despite an increased number of MG, tear film stability was not improved.

Conclusion

Through the intelligent quantitative algorithm, we found that AC leads to significant changes in MG structure, particularly affecting gland length and central area.

Artificial intelligence in corneal diseases: A narrative review

Corneal diseases represent a growing public health burden, especially in resource-limited settings lacking access to specialized eye care. Artificial intelligence (AI) offers promising solutions for automating the diagnosis and management of corneal conditions. This narrative review examines the application of AI in corneal diseases, focusing on keratoconus, infectious keratitis, pterygium, dry eye disease, Fuchs endothelial corneal dystrophy, and corneal transplantation. AI models integrating diverse imaging modalities (e.g., corneal topography, slit-lamp, and anterior segment OCT images) and clinical data have demonstrated high diagnostic accuracy, often outperforming human experts. Emerging trends include the incorporation of biomechanical data to enhance keratoconus detection, leveraging in vivo confocal microscopy for diagnosing infectious keratitis, and employing multimodal approaches for comprehensive disease analysis. Additionally, AI has shown potential in predicting disease progression, treatment outcomes, and postoperative complications in corneal transplantation. While challenges remain such as population heterogeneity, limited external validation, and the "black box" nature of some models, ongoing advancement in explainable AI, data augmentation, and improved regulatory frameworks can serve to address these limitations.

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.

Applications of Artificial Intelligence in Diagnosis of Dry Eye Disease: A Systematic Review and Meta-Analysis

PURPOSE

Clinical diagnosis of dry eye disease is based on a subjective Ocular Surface Disease Index questionnaire or various objective tests, however, these diagnostic methods have several limitations.

METHODS

We conducted a comprehensive review of articles discussing various applications of artificial intelligence (AI) models in the diagnosis of the dry eye disease by searching PubMed, Web of Science, Scopus, and Google Scholar databases up to December 2022. We initially extracted 2838 articles, and after removing duplicates and applying inclusion and exclusion criteria based on title and abstract, we selected 47 eligible full-text articles. We ultimately selected 17 articles for the meta-analysis after applying inclusion and exclusion criteria on the full-text articles. We used the Standards for Reporting of Diagnostic Accuracy Studies to evaluate the quality of the methodologies used in the included studies. The performance criteria for measuring the effectiveness of AI models included area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. We calculated the pooled estimate of accuracy using the random-effects model.

RESULTS

The meta-analysis showed that pooled estimate of accuracy was 91.91% (95% confidence interval: 87.46-95.49) for all studies. The mean (±SD) of area under the receiver operating characteristic curve, sensitivity, and specificity were 94.1 (±5.14), 89.58 (±6.13), and 92.62 (±6.61), respectively.

CONCLUSIONS

This study revealed that AI models are more accurate in diagnosing dry eye disease based on some imaging modalities and suggested that AI models are promising in augmenting dry eye clinics to assist physicians in diagnosis of this ocular surface condition.

A machine learning approach to predicting dry eye-related signs, symptoms and diagnoses from meibography images

Purpose

To use artificial intelligence to identify relationships between morphological characteristics of the Meibomian glands (MGs), subject factors, clinical outcomes, and subjective symptoms of dry eye.

Methods

A total of 562 infrared meibography images were collected from 363 subjects (170 contact lens wearers, 193 non-wearers). Subjects were 67.2 % female and were 54.8 % Caucasian. Subjects were 18 years of age or older. A deep learning model was trained to take meibography as input, segment the individual MG in the images, and learn their detailed morphological features. Morphological characteristics were then combined with clinical and symptom data in prediction models of MG function, tear film stability, ocular surface health, and subjective discomfort and dryness. The models were analyzed to identify the most heavily weighted features used by the algorithm for predictions.

Results

MG morphological characteristics were heavily weighted predictors for eyelid notching and vascularization, MG expressate quality and quantity, tear film stability, corneal staining, and comfort and dryness ratings, with accuracies ranging from 65 % to 99 %. Number of visible MG, along with other clinical parameters, were able to predict MG dysfunction, aqueous deficiency and blepharitis with accuracies ranging from 74 % to 85 %.

Conclusions

Machine learning-derived MG morphological characteristics were found to be important in predicting multiple signs, symptoms, and diagnoses related to MG dysfunction and dry eye. This deep learning method illustrates the rich clinical information that detailed morphological analysis of the MGs can provide, and shows promise in advancing our understanding of the role of MG morphology in ocular surface health.

Dual-Mode Imaging System for Early Detection and Monitoring of Ocular Surface Diseases

The global prevalence of ocular surface diseases (OSDs), such as dry eyes, conjunctivitis, and subconjunctival hemorrhage (SCH), is steadily increasing due to factors such as aging populations, environmental influences, and lifestyle changes. These diseases affect millions of individuals worldwide, emphasizing the importance of early diagnosis and continuous monitoring for effective treatment. Therefore, we present a deep learning-enhanced imaging system for the automated, objective, and reliable assessment of these three representative OSDs. Our comprehensive pipeline incorporates processing techniques derived from dual-mode infrared (IR) and visible (RGB) images. It employs a multi-stage deep learning model to enable accurate and consistent measurement of OSDs. This proposed method has achieved a 98.7% accuracy with an F1 score of 0.980 in class classification and a 96.2% accuracy with an F1 score of 0.956 in SCH region identification. Furthermore, our system aims to facilitate early diagnosis of meibomian gland dysfunction (MGD), a primary factor causing dry eyes, by quantitatively analyzing the meibomian gland (MG) area ratio and detecting gland morphological irregularities with an accuracy of 88.1% and an F1 score of 0.781. To enhance convenience and timely OSD management, we are integrating a portable IR camera for obtaining meibography during home inspections. Our system demonstrates notable improvements in expanding dual-mode image-based diagnosis for broader applicability, effectively enhancing patient care efficiency. With its automation, accuracy, and compact design, this system is well-suited for early detection and ongoing assessment of OSDs, contributing to improved eye healthcare in an accessible and comprehensible manner.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

Transfer learning for anatomical structure segmentation in otorhinolaryngology microsurgery

BACKGROUND

Reducing the annotation burden is an active and meaningful area of artificial intelligence (AI) research.

METHODS

Multiple datasets for the segmentation of two landmarks were constructed based on 41 257 labelled images and 6 different microsurgical scenarios. These datasets were trained using the multi-stage transfer learning (TL) methodology.

RESULTS

The multi-stage TL enhanced segmentation performance over baseline (mIOU 0.6892 vs. 0.8869). Besides, Convolutional Neural Networks (CNNs) achieved a robust performance (mIOU 0.8917 vs. 0.8603) even when the training dataset size was reduced from 90% (30 078 images) to 10% (3342 images). When directly applying the weight from one certain surgical scenario to recognise the same target in images of other scenarios without training, CNNs still obtained an optimal mIOU of 0.6190 ± 0.0789.

CONCLUSIONS

Model performance can be improved with TL in datasets with reduced size and increased complexity. It is feasible for data-based domain adaptation among different microsurgical fields.

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.

Role of instrumental factors in Meibomian gland contrast assessment

PURPOSE

Meibomian gland contrast has been suggested as a potential biomarker in Meibomian gland dysfunction. This study analysed the instrumental factors related to contrast. The objectives were to determine whether the mathematical equations used to compute gland contrast (e.g., Michelson or Yeh and Lin), impact the ability to identify abnormal individuals, to ascertain whether contrast between the gland and the background could be an effective biomarker and to assess whether using contrast-enhancement on the gland image improves its diagnostic efficacy.

METHODS

A total of 240 meibography images from 40 participants (20 controls and 20 having Meibomian gland dysfunction or blepharitis), were included. The Oculus Keratograph 5M was used to capture images from the upper and lower eyelids of each eye. The contrast of unprocessed images and those pre-processed with contrast-enhancement algorithms were analysed. Contrast was measured on the eight central glands. Two equations for contrast computation were used, and the contrast both between glands and within a gland were calculated.

RESULTS

Significant differences were found between the groups for inter-gland area in the upper (p = 0.01) and lower eyelids (p = 0.001) for contrast measured with the Michelson formula. Similar effects were observed when using the Yeh and Lin method in the upper (p = 0.01) and lower eyelids (p = 0.04). These results were obtained for images enhanced with the Keratograph 5M algorithm.

CONCLUSIONS

Meibomian gland contrast is a useful biomarker of disease related to the Meibomian glands. Contrast measurement should be determined using contrast-enhanced images in the inter-gland area. However, the method used to compute contrast did not influence the results.

Impacts of gender and age on meibomian gland in aged people using artificial intelligence

Purpose: To evaluate the effects of age and gender on meibomian gland (MG) parameters and the associations among MG parameters in aged people using a deep-learning based artificial intelligence (AI). Methods: A total of 119 subjects aged ≥60 were enrolled. Subjects completed an ocular surface disease index (OSDI) questionnaire, received ocular surface examinations including Meibography images captured by Keratograph 5M, diagnosis of meibomian gland dysfunction (MGD) and assessment of lid margin and meibum. Images were analyzed using an AI system to evaluate the MG area, density, number, height, width and tortuosity. Results: The mean age of the subjects was 71.61 ± 7.36 years. The prevalence of severe MGD and meibomian gland loss (MGL) increased with age, as well as the lid margin abnormities. Gender differences of MG morphological parameters were most significant in subjects less than 70 years old. The MG morphological parameters detected by AI system had strong relationship with the traditional manual evaluation of MGL and lid margin parameters. Lid margin abnormities were significantly correlated with MG height and MGL. OSDI was related to MGL, MG area, MG height, plugging and lipid extrusion test (LET). Male subjects, especially the ones who smoke or drink, had severe lid margin abnormities, and significantly decreased MG number, height, and area than the females. Conclusion: The AI system is a reliable and high-efficient method for evaluating MG morphology and function. MG morphological abnormities developed with age and were worse in the aging males, and smoking and drinking were risk factors.

The influence of overnight orthokeratology and soft contact lens on the meibomian gland evaluated using an artificial intelligence analytic system

PURPOSE

To test the changes of meibomian gland (MG) morphology using an artificial intelligence (AI) analytic system in asymptomatic children wearing overnight orthokeratology (OOK) and soft contact lens (SCL).

METHODS

A retrospective study was conducted including 89 participants treated with OOK and 70 participants with SCL. Tear meniscus height (TMH), noninvasive tear breakup time (NIBUT), and meibography were obtained using Keratograph 5 M. MG tortuosity, height, width, density, and vagueness value were measured using an artificial intelligence (AI) analytic system.

RESULTS

In an average of 20.80 ± 10.83 months follow-up, MG width of the upper eyelid significantly increased and MG vagueness value significantly decreased after OOK and SCL treatment (all P < 0.05). MG tortuosity of the upper eyelid significantly increased after OOK treatment (P < 0.05). TMH and NIBUT did not differ significantly pre- and post- OOK and SCL treatment (all P > 0.05). The results from the GEE model demonstrated that OOK treatment positively affected MG tortuosity of both upper and lower eyelids (P < 0.001; P = 0.041, respectively) and MG width of the upper eyelid (P = 0.038), while it negatively affected MG density of the upper eyelid (P = 0.036) and MG vagueness value of both upper and lower eyelids (P < 0.001; P < 0.001, respectively). SCL treatment positively affected MG width of both upper and lower eyelids (P < 0.001; P = 0.049, respectively) as well as MG height of the lower eyelid (P = 0.009) and tortuosity of the upper eyelid, (P = 0.034) while it negatively affected MG vagueness value of both upper and lower eyelids (P < 0.001; P < 0.001, respectively). However, no significant relationship was found between the treatment duration and TMH, NIBUT, MG morphological parameters in OOK group. SCL treatment duration negatively affected MG height of the lower eyelid (P = 0.002).

CONCLUSIONS

OOK and SCL treatment in asymptomatic children can influence MG morphology. The AI analytic system may be an effective method to facilitate the quantitative detection of MG morphological changes.

A Deep Learning Model for Evaluating Meibomian Glands Morphology from Meibography

To develop a deep learning model for automatically segmenting tarsus and meibomian gland areas on meibography, we included 1087 meibography images from dry eye patients. The contour of the tarsus and each meibomian gland was labeled manually by human experts. The dataset was divided into training, validation, and test sets. We built a convolutional neural network-based U-net and trained the model to segment the tarsus and meibomian gland area. Accuracy, sensitivity, specificity, and receiver operating characteristic curve (ROC) were calculated to evaluate the model. The area under the curve (AUC) values for models segmenting the tarsus and meibomian gland area were 0.985 and 0.938, respectively. The deep learning model achieved a sensitivity and specificity of 0.975 and 0.99, respectively, with an accuracy of 0.985 for segmenting the tarsus area. For meibomian gland area segmentation, the model obtained a high specificity of 0.96, with high accuracy of 0.937 and a moderate sensitivity of 0.751. The present research trained a deep learning model to automatically segment tarsus and the meibomian gland area from infrared meibography, and the model demonstrated outstanding accuracy in segmentation. With further improvement, the model could potentially be applied to assess the meibomian gland that facilitates dry eye evaluation in various clinical and research scenarios.

Artificial intelligence-assisted diagnosis of ocular surface diseases

With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.

Meibomian gland morphological changes in ocular herpes zoster patients based on AI analysis

Varicella-zoster virus (VZV) infections result in a series of ophthalmic complications. Clinically, we also discover that the proportion of dry eye symptoms was significantly higher in patients with herpes zoster ophthalmicus (HZO) than in healthy individuals. Meibomian gland dysfunction (MGD) is one of the main reasons for dry eye. Therefore, we hypothesize that HZO may associate with MGD, affecting the morphology of meibomian gland (MG) because of immune response and inflammation. The purpose of this study is to retrospectively analyze the effect of HZO with craniofacial herpes zoster on dry eye and MG morphology based on an Artificial intelligence (AI) MG morphology analytic system. In this study, 26 patients were diagnosed as HZO based on a history of craniofacial herpes zoster accompanied by abnormal ocular signs. We found that the average height of all MGs of the upper eyelid and both eyelids were significantly lower in the research group than in the normal control group (p &lt; 0.05 for all). The average width and tortuosity of all MGs for both upper and lower eyelids were not significantly different between the two groups. The MG density of the upper eyelid and both eyelids were significantly lower in the HZO group than in the normal control group (p = 0.020 and p = 0.022). Therefore, HZO may lead to dry eye, coupled with the morphological changes of MGs, mainly including a reduction in MG density and height. Moreover, it is important to control HZO early and timely, which could prevent potential long-term severe ocular surface injury.

Editorial: Imaging in Ophthalmology

Over the last decade, ophthalmology has significantly benefited from advances in vivo non-invasive ophthalmic imaging techniques that play currently a fundamental role in the clinical assessment, diagnosis, management, and monitoring of a wide variety of conditions involving both the anterior and posterior segment [...]

Uneven Meibomian Gland Dropout in Patients with Meibomian Gland Dysfunction and Demodex Infestation

The purpose of this study was to compare the differences between uneven meibomian gland (MG) atrophy with and without Demodex infestation based on the index of uneven atrophy score (UAS). In this retrospective cohort study, 158 subjects were recruited, including 66 subjects in the Demodex-positive MGD group, 49 subjects in the Demodex-negative MGD group, and 43 subjects as normal control. No significant difference was verified in OSDI, TMH, TBUT, CFS, lid margin score, and meibograde (all p > 0.05) between the Demodex-positive MGD group and the Demodex-negative MGD group. The UAS index of the upper eyelid or both eyelids was significantly higher in the Demodex-positive group in comparison with the normal control group and Demodex-negative group and the difference was statistically significant between the three groups. The UAS was significantly positive correlation with OSDI (r = 0.209, p < 0.05), lid margin score (r = 0.287, p < 0.001), and meibograde (r = 0.356, p < 0.001), which has a significant negative correlation with TBUT (r = −0.248, p < 0.05). Thus, Demodex infestation can cause uneven MG atrophy and we propose a novel index of UAS, which is used to evaluate uneven atrophy of MGs and as a morphological index of Demodex infestation.

Uneven Index: A Digital Biomarker to Prompt Demodex Blepharitis Based on Deep Learning

Purpose: To evaluate ocular surface manifestations and morphological changes in meibomian glands (MGs) based on artificial intelligence (AI) analysis in patients with Demodex blepharitis.

Methods: In this retrospective study, 115 subjects were enrolled, including 64 subjects with Demodex blepharitis and 51 subjects without Demodex blepharitis as control group. Morphological indexes were evaluated for height, width, tortuosity, MG density, total variation, and the three types of corrected total variation as Uneven indexes.

Results: There were no statistically significant differences in all MGs’ average tortuosity and width between the two groups. The average height of all MGs and MG density were significantly lower in the Demodex blepharitis group than control group. The total variation and two types of Uneven indexes were significantly higher in the Demodex blepharitis group than in the control group. Especially the Uneven Index of total variation/MG density had an AUC of 0.822. And the sensitivity and specificity were 59.4% and 92.2%, respectively, at a cut-off value of 3971.667. In addition, Demodex blepharitis was associated with significantly lower meibum quality and expressibility, severe atrophy of MGs, a higher ocular surface disease index (OSDI), and more instability of the tear film.

Conclusion:Demodex mites are strongly associated with morphological changes in the MGs and may cause uneven gland atrophy. Therefore, the novel characteristic parameter, the Uneven index, may serve as a digital biomarker to evaluate uneven atrophy of MGs and prompt Demodex blepharitis.