Glaucoma

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 health. However, current studies exclude neurodegenerative cases in neuropathy diagnosis and rely on 'black box' models. To address this, we applied explainable machine learning to optical coherence tomography (OCT) data, integrating a privacy-prese...

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 telemetric IOP sensor. DESIGN: A prospective, open-label, single-arm, multicenter study. SUBJECTS: Twenty-four patients were enrolled, including 20 with primary open-angle glaucoma, 2 with pseudoexfoliative, 1 with pigmentary, and 1 with uveitic glaucoma. M...

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 ...

Predicting worsening visual field via masked dual stage attention recurrent neural network.

Based on dual-stage attention recurrent neural network (DA-RNN), a masked DA-RNN (MDA-RNN) model was developed to predict future visual fields (VF). T...

Therapeutic ultrasound in ophthalmology: a comprehensive review.

BACKGROUND: Therapeutic ultrasound has emerged as a promising noninvasive or minimally invasive modality in ophthalmology, offering novel solutions ac...

A hybrid dual-stream CNN framework with dynamic data augmentation and improved Manta Ray Foraging Optimization for robust glaucoma detection.

A progressive neurological condition, glaucoma is one of the main causes of irreversible blindness in the globe. Early detection is crucial to prevent...

Deep learning in population-based screening for glaucoma.

BACKGROUND/AIMS: Deep learning algorithms have shown promise for glaucoma detection using retinal imaging. The Northern Finland Birth Cohort Eye Study...

A hybrid framework for effective microscopic cell counting segmentation integrating Light-U-net with watershed.

Glaucoma is a major cause of irreversible blindness worldwide, resulting in the progressive degeneration of retinal ganglion cellss (RGCs), which make...

Predicting 10-2 SITA Standard from 24-2C SITA Faster: Development and Validation of Deep24-2C.

PURPOSE: To develop and validate Deep24-2C, a machine learning (ML) model that reconstructs comprehensive Humphrey Field Analyzer (HFA) 10-2 visual fi...

MaxGRNet: A multi-axis vision transformer with improved generalization for eye disease classification using explainable AI with insertion-deletion operations on fundus images.

Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impa...

Integrating generative AI and genetic causal inference identifies metformin as a repurposing candidate for normal tension glaucoma.

The normal tension glaucoma (NTG) has limited drug options since current antiglaucoma medications are mostly designed to decrease intraocular pressure...