Glaucoma

Proteomic clocks combined with deep learning phenotypes track eye aging and diseases.

Proteomics represents a powerful but underutilized approach for characterizing eye aging. Here, leveraging data from three large-scale, cross-national cohorts of over 55,000 transethnic participants, we demonstrate the ability of high-throughput proteomics combined with deep learning (DL) phenotyping to track eye aging and disease in both discovery and external validation settings. Proteomic aging...

Plant-based diet quality and risk of age-related eye diseases: evidence from multi-cohorts with multi-omics insights.

Plant-based diets may influence age-related eye diseases (AREDs), but whether ocular benefits depend on diet quality remains unclear. We examined associations of a healthy plant-based diet index (PDI-H) and an unhealthy plant-based diet index (PDI-U) with age-related macular degeneration (AMD), cataract, glaucoma, diabetic retinopathy (DR), and retinal vein occlusion (RVO) using the UK Biobank pro...

Update on the structure-Function relationship in glaucoma.

The relationship between structural and functional damage in glaucoma, the structure-function relationship, forms the cornerstone of disease assessmen...

Clinical determinants of retinal age gap estimated from fundus photographs in glaucoma patients.

Retinal age gap (RAG), defined as the difference between artificial intelligence-predicted retinal age and chronological age derived from fundus photo...

Combined diagnostic accuracy of two artificial intelligence systems for glaucoma diagnosis using color fundus photography.

PURPOSE: To evaluate the diagnostic accuracy of two commercially available artificial intelligence (AI) systems based on color fundus photography (CFP...

Image-Quality-Aware Multimodal Artificial Intelligence for Automated Structured OCT Report Generation in Glaucoma Evaluation.

OBJECTIVE: To develop an explainable multimodal large language model (MM-LLM) that (1) screens optic nerve head (ONH) OCT circle scans for quality and...

OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.

Glaucoma shares similarities with neurodegenerative conditions like dementia, Parkinson's disease, and ischaemic optic neuropathy, which affect ocular...

Fusing Structural Phenotypes with Functional Data for Early Prediction of Primary Angle-Closure Glaucoma Progression.

PURPOSE: To classify eyes as slow or fast glaucoma progressors in patients with primary angle-closure glaucoma (PACG) using an integrated approach com...

Deep Learning and Radiomics Assessment for Highly Myopic Glaucoma Detection Based on Fundus Photography.

PURPOSE: To investigate spatially distinctive features in fundus photographs of highly myopic glaucoma (HMG) by integrating radiomics and deep learnin...

Machine Learning-Based Prediction of Long-Term Intraocular Pressure Fluctuations in Open-Angle Glaucoma.

OBJECTIVE: To investigate the predictability of long-term intraocular pressure (IOP) fluctuations in open-angle glaucoma eyes implanted with a telemet...

STAGE challenge: Structural-Functional Transition in Glaucoma Assessment.

Glaucoma is a leading cause of irreversible yet preventable blindness in working-age populations. Clinical diagnos is currently relies on functional v...

Structure-aware retinal disentanglement reveals the genetic architecture of ocular and systemic diseases.

Deep learning effectively extracts retinal phenotypes but often functions as an entangled black box, obscuring specific genetic mechanisms and hinderi...

AUBADE-syn: a novel deep learning ensemble method for glaucoma detection using synthetic fundus images on imbalanced datasets.

Glaucoma is the leading cause of irreversible blindness worldwide. Early detection is essential to preserve vision. Deep learning approaches have show...

Dynamic brain states and molecular signatures in primary angle-closure glaucoma.

BACKGROUND: Primary angle-closure glaucoma (PACG) has traditionally been regarded as an ocular disorder, but accumulating evidence suggests broader ce...

Comparing the Use of Measured and Smoothed Data in Forecasting Visual Field Tests Using Deep Learning.

OBJECTIVE: To evaluate the impact of training and testing deep learning (DL) models for visual field (VF) forecasting using input-target pairs in whic...

Global approval and certification of ophthalmic AI devices: A comparative regulatory perspective.

Artificial intelligence (AI) tools are rapidly reshaping ophthalmology by improving screening and diagnosis for diabetic retinopathy, age-related macu...

Surgical technology update for ophthalmic surgery: heads-up display, artificial intelligence, instrumentation, and robotics.

PURPOSE OF REVIEW: Rapid advances in surgical visualization, microsurgical instrumentation, artificial intelligence (AI), and robotic assistance are r...

Explainable AI-driven diagnosis model for early glaucoma detection using grey-wolf optimized extreme learning machine approach.

Glaucoma is a leading global cause of blindness, making early detection essential. This paper introduces GlaucoXAI (Glaucoma Explainable AI), an advan...

Integrated plasma proteomics and metabolomics reveal immunometabolic pathways and predictive signatures for age-related eye diseases.

BACKGROUND AND AIMS: Age-related eye diseases (AREDs) share aging as a major risk factor, but the systemic molecular changes preceding disease onset r...

The role of autophagy in ocular health: mechanisms, pathologies, and therapeutic strategies.

Autophagy is a self-digestive process in which cellular components are degraded and recycled to maintain homeostasis and cope with stress. When cells ...