Glaucoma

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 diseases and can lead to permanent vision loss if left untreated. In this paper, a new hybrid model has been proposed with the methods accepted in the literature used in the early diagnosis of these diseases. The relationships between imaging analyses and ...

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 to stop vision loss. Manually evaluating retinal fundus images frequently necessitates considerable effort and is prone to observer-dependent discrepancies. To address these constraints, a new automated method for detecting glaucoma is presented. It ...

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...

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...

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...

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...

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 asso...

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...