Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks

The applications of anterior segment optical coherence tomography in glaucoma: a 20-year bibliometric analysis

Objective

In the past 20 years, the research application of anterior segment optical coherence tomography (AS-OCT) in the field of glaucoma has become a hot topic, but there is still a lack of bibliometric reports on this scientific field. The aim of this study is to explore the research hotspots and trends in the field using bibliometric methods.

Method

Analyzing literature from 2004 to 2023 on AS-OCT in glaucoma within the SCI database, this study utilized Bibliometric, VOS viewer, and Cite Space for a comprehensive bibliometric analysis covering document counts, countries, institutions, journals, authors, references, and keywords.

Results

A total of 931 eligible articles were collected, showing a continuous increase in annual research output over the past 20 years. The United States, China, and Singapore were the top three countries in terms of publication volume, with 288, 231, and 124 articles, respectively, and there was close cooperation among these countries. The NATIONAL UNIVERSITY OF SINGAPORE, SUN YAT SEN UNIVERSITY, and SINGAPORE NATIONAL EYE CENTRE were the most productive institutions with 93, 92, and 87 articles, respectively. JOURNAL OF GLAUCOMA, INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, and OPHTHALMOLOGY were the journals with the highest number of publications, with 86, 69, and 46 articles, respectively. PROGRESS IN RETINAL AND EYE RESEARCH, published in the United States, was the top-cited journal. Researchers Aung Tin, He Mingguang, and David S. Friedman were highlighted for their contributions. The reference clustering was divided into 12 categories, among which "deep learning, anterior segment" were the most cited categories. The keywords of research frontiers include deep learning, classification, progression, and management.

Conclusion

This article analyses the academic publications on AS-OCT in the diagnosis and treatment of glaucoma over the last 20 years. Among them, the United States contributed the largest number of publications in this field, with the highest number of literature citations and mediated centrality. Among the prolific authors, aung, tin topped the list with 77 publications and 3,428 citations. Since the beginning of 2018, advances in artificial intelligence have shifted the focus of research in this field from manual measurements to automated detection and identification of relevant indicators.

Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI's potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI's vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State

Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease's development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers.

Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images

Glaucoma is the dominant reason for irreversible blindness worldwide, and its best remedy is early and timely detection. Optical coherence tomography has come to be the most commonly used imaging modality in detecting glaucomatous damage in recent years. Deep Learning using Optical Coherence Tomography Modality helps in predicting glaucoma more accurately and less tediously. This experimental study aims to perform glaucoma prediction using eight different ImageNet models from Optical Coherence Tomography of Glaucoma. A thorough investigation is performed to evaluate these models' performances on various efficiency metrics, which will help discover the best performing model. Every net is tested on three different optimizers, namely Adam, Root Mean Squared Propagation, and Stochastic Gradient Descent, to find the best relevant results. An attempt has been made to improvise the performance of models using transfer learning and fine-tuning. The work presented in this study was initially trained and tested on a private database that consists of 4220 images (2110 normal optical coherence tomography and 2110 glaucoma optical coherence tomography). Based on the results, the four best-performing models are shortlisted. Later, these models are tested on the well-recognized standard public Mendeley dataset. Experimental results illustrate that VGG16 using the Root Mean Squared Propagation Optimizer attains auspicious performance with 95.68% accuracy. The proposed work concludes that different ImageNet models are a good alternative as a computer-based automatic glaucoma screening system. This fully automated system has a lot of potential to tell the difference between normal Optical Coherence Tomography and glaucomatous Optical Coherence Tomography automatically. The proposed system helps in efficiently detecting this retinal infection in suspected patients for better diagnosis to avoid vision loss and also decreases senior ophthalmologists' (experts) precious time and involvement.

Hybrid Variation-Aware Network for Angle-Closure Assessment in AS-OCT

Automatic angle-closure assessment in Anterior Segment OCT (AS-OCT) images is an important task for the screening and diagnosis of glaucoma, and the most recent computer-aided models focus on a binary classification of anterior chamber angles (ACA) in AS-OCT, i.e., open-angle and angle-closure. In order to assist clinicians who seek better to understand the development of the spectrum of glaucoma types, a more discriminating three-class classification scheme was suggested, i.e., the classification of ACA was expended to include open-, appositional- and synechial angles. However, appositional and synechial angles display similar appearances in an AS-OCT image, which makes classification models struggle to differentiate angle-closure subtypes based on static AS-OCT images. In order to tackle this issue, we propose a 2D-3D Hybrid Variation-aware Network (HV-Net) for open-appositional-synechial ACA classification from AS-OCT imagery. Specifically, taking into account clinical priors, we first reconstruct the 3D iris surface from an AS-OCT sequence, and obtain the geometrical characteristics necessary to provide global shape information. 2D AS-OCT slices and 3D iris representations are then fed into our HV-Net to extract cross-sectional appearance features and iris morphological features, respectively. To achieve similar results to those of dynamic gonioscopy examination, which is the current gold standard for diagnostic angle assessment, the paired AS-OCT images acquired in dark and light illumination conditions are used to obtain an accurate characterization of configurational changes in ACAs and iris shapes, using a Variation-aware Block. In addition, an annealing loss function was introduced to optimize our model, so as to encourage the sub-networks to map the inputs into the more conducive spaces to extract dark-to-light variation representations, while retaining the discriminative power of the learned features. The proposed model is evaluated across 1584 paired AS-OCT samples, and it has demonstrated its superiority in classifying open-, appositional- and synechial angles.

Novel Approaches for Imaging-Based Diagnosis of Ocular Surface Disease

Imaging has become indispensable in the diagnosis and management of diseases in the posterior part of the eye. In recent years, imaging techniques for the anterior segment are also gaining importance and are nowadays routinely used in clinical practice. Ocular surface disease is often synonymous with dry eye disease, but also refers to other conditions of the ocular surface, such as Meibomian gland dysfunction or keratitis and conjunctivitis with different underlying causes, i.e., allergies or infections. Therefore, correct differential diagnosis and treatment of ocular surface diseases is crucial, for which imaging can be a helpful tool. A variety of imaging techniques have been introduced to study the ocular surface, such as anterior segment optical coherence tomography, in vivo confocal microscopy, or non-contact meibography. The present review provides an overview on how these techniques can be used in the diagnosis and management of ocular surface disease and compares them to clinical standard methods such as slit lamp examination or staining of the cornea or conjunctiva. Although being more cost-intensive in the short term, in the long term, the use of ocular imaging can lead to more individualized diagnoses and treatment decisions, which in turn are beneficial for affected patients as well as for the healthcare system. In addition, imaging is more objective and provides good documentation, leading to an improvement in patient follow-up and education.