Glaucoma

Smartphone-Based Portable Slit Lamp in Anterior Segment Diseases: A Narrative Review of Clinical Assessment and Integration with Artificial Intelligence.

Smartphone-based portable slit lamps are rapidly evolving from simple add-ons into practical, low-cost front-line tools for anterior segment care beyond traditional clinical settings. By integrating high-performance smartphone cameras with compact optical attachments (e.g., slit-light converters, macro lenses, blue filters) and dedicated applications, these devices deliver slit-lamp-style imaging ...

SwinCup-DiscNet: A fusion transformer framework for glaucoma diagnosis using optic disc and cup features.

Glaucoma remains a critical cause of permanent global visual disability, and is produced by advancing destruction of the visual nerve head (ONH). Early detection is critical important in preventing vision loss. We propose a new fusion transformer pipeline, which integrates optic disc/cup and feature-based segmentation to aid in the effective screening of glaucoma, in this paper. The proposed appro...

Development of deep learning model to screen for primary open-angle glaucoma in African ancestry individuals.

Primary open-angle glaucoma (POAG) screening using artificial intelligence (AI) has emerged as a transformative method to identify undiagnosed disease...

Offline AI-assisted fundus imaging for preliminary screening of childhood glaucoma.

PURPOSE: To evaluate the feasibility and performance of AI-assisted portable fundus photography in children with glaucoma. METHODS: This case series d...

GDPooled transformer: glaucoma detection using pooled attention based transformer with attention mechanism.

Glaucoma is a common eye disease affecting several people worldwide. Blindness can be avoided with proper treatment and regular examination. Delayed d...

A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.

Glaucoma is the leading cause of irreversible blindness worldwide with heterogeneous progression rates. Artificial Intelligence (AI) may enable accura...

A comparative evaluation of parafoveal and perifoveal macular vessel density loss in glaucoma using 3 × 3 mm OCTA scans.

In a prior study we demonstrated the strong performance of convolutional neural networks (CNNs) in distinguishing healthy from glaucomatous eyes and s...

Awareness, trust, and expectations of AI for glaucoma care among Bulgarian ophthalmologists: Role of demographic factors.

Artificial intelligence (AI) holds promise for enhancing glaucoma screening and management, yet its adoption depends on clinician perceptions, particu...

Ligand-based machine learning models to classify active compounds for prostaglandin EP2 receptor.

Prostaglandin receptors are pharmacologically validated targets with implications in several medical indications, including glaucoma, cardiac cyanotic...

Predicting HFA 30-2 Visual Fields with Deep Learning from Multimodal OCT-Fundus Feature Fusion and Structure-Function Discordance Analysis.

Glaucoma is a leading cause of irreversible vision loss. During clinical follow-up, visual field (VF) tests (Humphrey Field Analyzer 30-2) assesses fu...

Classifying retinal images via vascular-optic disc cross-segmentation and attentive feature selection.

The prevalence of retinal disorders is rising at an alarming rate, posing a significant risk of irreversible blindness without timely intervention. Re...

To see or not to see the vet: A vignette-based study of decision-making by UK dog owners regarding seeking veterinary care for commonly presenting conditions.

Barriers to accessing veterinary-care for dog-owners are diverse and dynamic, and widely accepted as major canine welfare threats because of potential...

Artificial Intelligence in Neurology and Neuro-Ophthalmology: Current Applications and Future Directions.

Artificial intelligence (AI) is revolutionizing neuro-ophthalmology by enhancing diagnostic accuracy and clinical decision-making. Techniques like dee...

Reply to Commentary on ``Development and Evaluation of an Artificial Intelligence Model to Set Target IOP for Glaucoma''.

Comment on: ``Development and Evaluation of an Artificial Intelligence Model to Set Target IOP for Glaucoma''.

Artificial Intelligence-Guided Endpoint Selection for Neuroprotection Trials in Glaucoma.

OBJECTIVE: Standard Automated Perimetry (SAP) is the primary method for monitoring glaucoma progression and an established functional endpoint in clin...

An Artificial Intelligence-Based Prognostic Model for Prediction of Functional Glaucoma Progression From Clinical and Structural Data.

PURPOSE: Integration of various sources of information for prediction of disease progression is an unmet need in glaucoma diagnostics. We designed a d...

Convolutional Graph Isomorphism Network to Detect Glaucomatous Visual Field Defects.

PURPOSE: To evaluate the performance of a deep learning (DL) model based on graph isomorphism networks (GINs) for detecting glaucomatous visual field ...

Evaluation of Glaucoma Treatment Information on Social Media Using Large Language Models.

PRCIS: This study investigates the accuracy, readability, utility, and educational value of glaucoma treatment content on social media platforms and e...

Big data in ophthalmology: comparative databases and research applications.

PURPOSE OF REVIEW: This review aims to highlight the expanding role of big data in ophthalmology, provide a comparison of the most prominent databases...