Glaucoma

Clinically aligned artificial intelligence for glaucoma diagnosis: enhancing retinal nerve fibre layer interpretation from fundus images.

BACKGROUND/OBJECTIVES: This study aimed to develop an interpretable artificial intelligence (AI) screening system that replicates a specialist's evaluation of fundus photographs. The system analyses three signs: visible retinal nerve fibre layer (RNFL) defects (as observed on fundus photography, not OCT-based measurement), vertical cup-to-disc ratio (VCDR) and rim-to-disc ratio (RDR). SUBJECTS/MET...

[Telemetric self-monitoring of intraocular pressure: from vision to reality? : Insights from the EyeMate-SC sensor].

BACKGROUND: The EyeMate-SC (G-Metrics GmbH, Hanover, Germany) is a permanently implantable microsensor positioned in the suprachoroidal space for telemetric, high-frequency self-measurement of intraocular pressure (IOP), with measurements transmitted to the treating ophthalmologist via an external reading device. OBJECTIVE: The aim of this review is to summarize the evidence regarding the safety o...

A hybrid model for early diagnosis of ophthalmology diseases leveraging CNNs, SBOA optimization, and XAI for visualization.

Ophthalmology diseases are among the leading causes of vision loss worldwide. Glaucoma, diabetic retinopathy, and cataracts are the most common diseas...

Artificial Intelligence for Predicting Postoperative Outcomes in Anterior Segment and Ocular Adnexa Surgery: A Systematic Review.

TOPIC: Artificial intelligence (AI) is increasingly applied to support decision-making in ophthalmology. This review evaluates the ability of AI to pr...

Fractal Sierpinski triangle block division for retina-based glaucoma detection using an optimized hybrid deep learning model.

Glaucoma is a primary cause of permanent vision loss, and it often gets worse without anybody noticing. This makes it very important to find it early ...

CrossSG-DTA: Synergizing Sequence Semantics and Graph Structures via Cross-Attention for Drug-Target Affinity Prediction.

Accurate prediction of drug-target affinities (DTA) is critical for drug discovery. However, this task remains a significant challenge due to the comp...

Automated Optic Disc Tilt Classification in Fundus Photographs Using Segmentation and the Elliptical Ratio: External Clinical Validation Study.

BACKGROUND: Optic disc tilt is a morphological change in myopic eyes that complicates clinical interpretation and artificial intelligence (AI)-based a...

Dual branch fundus deep learning network as an enhanced multi classification system for ocular disease detection via hybrid feature fusion.

Ophthalmic diagnosis relies heavily on the interpretation of fundus images to identify a range of debilitating diseases. However, the presence of mult...

Deep learning in glaucoma referral: Performance assessment using a real-world setting.

PURPOSE: Evaluate a deep learning model's performance as a pre-referral filter for referable glaucoma using colour fundus photographs. METHODS: Retros...

Automated iridocorneal angle classification using a multimodal large language model.

PURPOSE: To evaluate the diagnostic performance of a general-purpose vision-language model (GPT-4o) in interpreting gonioscopic images of the anterior...

Combining structural and microvascular parameters via machine learning for enhanced diagnosis of normal-tension glaucoma among highly myopic eyes: a prospective cross-sectional diagnostic accuracy study.

PURPOSE: This study aimed to evaluate whether a combination of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters could improve ...

Smartphone-based offline AI for multi-disease retinal screening: Real-world accuracy.

ObjectiveTo evaluate the diagnostic accuracy of a multi-disease offline artificial intelligence system (Medios-AI, MAI), integrated into a smartphone-...

Hypertuned boosting approach with Local Binary Pattern and Pivot Distribution Count method feature extractor for glaucoma identification.

Glaucoma, the second largest cause of irreversible blindness worldwide, causes significant damage to the optic nerve. Early diagnosis of glaucoma is c...

Contribution of resting pulse rate to fall risk prediction in patients with glaucoma: a nationwide retrospective study based on an XGBoost model.

BACKGROUND: Falls are among the most common safety concerns in people with visual impairment and can lead to serious consequences, including fractures...

Artificial Intelligence-Driven Multimodal Prediction of 10-Year Incident Glaucoma Integrating Genetic and Deep Learning-Derived Imaging Features.

PURPOSE: Glaucoma is the leading cause of irreversible blindness worldwide. It often remains asymptomatic until advanced stages. Hence, accurate predi...

DeepSeek-assisted problem-based learning for glaucoma education in an undergraduate ophthalmology clerkship: a randomized educational pilot study.

This study evaluated a teaching approach that combines the open-source large language model (LLM) DeepSeek with problem-based learning (PBL) in a glau...

The diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care: a prospective validation study.

OBJECTIVES: This study investigated the diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care, using ophthalmologist-led s...

Evaluating reasoning in multimodal large language models for ophthalmology: a bilingual benchmark study using clinical vignettes and imaging.

BACKGROUND: Large language models (LLMs) excel in text-based medical exams, but their ability to integrate multimodal data, critical for ophthalmology...

The Future of Glaucoma: Data, Artificial Intelligence, and Precision Medicine for Patient-Centered Care.

PURPOSE: To summarize the main topics discussed during the 29th Annual Optic Nerve Rescue and Restoration Think Tank Meeting "The Future of Glaucoma: ...

Development of a machine learning model using systemic and ophthalmic parameters to detect sleep-disordered breathing in glaucoma patients.

PURPOSE: To develop and validate a machine-learning model using systemic and ophthalmic parameters that predicts sleep-disordered breathing (SDB) in p...