Glaucoma

The performance of ChatGPT-4 and Bing Chat in frequently asked questions about glaucoma.

PurposeTo evaluate the appropriateness and readability of the responses generated by ChatGPT-4 and Bing Chat to frequently asked questions about glaucoma.MethodThirty-four questions were generated for this study. Each question was directed three times to a fresh ChatGPT-4 and Bing Chat interface. The obtained responses were categorised by two glaucoma specialists in terms of their appropriateness....

AI for glaucoma, Are we reporting well? a systematic literature review of DECIDE-AI checklist adherence.

BACKGROUND/OBJECTIVES: This systematic literature review examines the quality of early clinical evaluation of artificial intelligence (AI) decision support systems (DSS) reported in glaucoma care. Artificial Intelligence applications within glaucoma care are increasing within the literature. For such DSS, there needs to be standardised reporting to enable faster clinical adaptation. In May 2022, a...

Capsule network-based deep learning for early and accurate diabetic retinopathy detection.

Glaucoma, an optic nerve disease resulting in blindness if left untreated, is a difficult condition in healthcare in view of its diagnostic difficulti...

Predicting visual field global and local parameters from OCT measurements using explainable machine learning.

Glaucoma is characterised by progressive vision loss due to retinal ganglion cell deterioration, leading to gradual visual field (VF) impairment. The ...

Mathematical Modeling and Artificial Intelligence to Explore Connections Between Glaucoma and the Gut Microbiome.

Glaucoma is a major cause of irreversible blindness, with primary open-angle glaucoma (POAG) being the most prevalent form. While elevated intraocula...

Artificial intelligence support improves diagnosis accuracy in anterior segment eye diseases.

CorneAI, a deep learning model designed for diagnosing cataracts and corneal diseases, was assessed for its impact on ophthalmologists' diagnostic acc...

Machine learning prediction of glaucoma by heavy metal exposure: results from the National Health and Nutrition Examination Survey 2005 to 2008.

Using follow-up data from the National Health and Nutrition Examination Survey (NHANES) database, we have collected information on 2572 subjects and u...

Rim learning framework based on TS-GAN: A new paradigm of automated glaucoma screening from fundus images.

Glaucoma detection from fundus images often relies on biomarkers such as the Cup-to-Disc Ratio (CDR) and Rim-to-Disc Ratio (RDR). However, precise seg...

Efficient diagnosis of retinal disorders using dual-branch semi-supervised learning (DB-SSL): An enhanced multi-class classification approach.

The early diagnosis of retinal disorders is essential in preventing permanent or partial blindness. Identifying these conditions promptly guarantees e...

Code-Free Deep Learning Glaucoma Detection on Color Fundus Images.

OBJECTIVE: Code-free deep learning (CFDL) allows clinicians with no coding experience to build their own artificial intelligence models. This study as...

Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design.

BACKGROUND: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experienc...

On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models.

Glaucoma is an irreversible, progressive, degenerative eye disorder arising because of increased intraocular pressure, resulting in eventual vision lo...

OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.

Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma di...

Optimising deep learning models for ophthalmological disorder classification.

Fundus imaging, a technique for recording retinal structural components and anomalies, is essential for observing and identifying ophthalmological dis...

Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets.

OBJECTIVE: For studies using real-world data, accurately identifying patients with phenotypes of interest is challenging. To identify cohorts of inter...

Systematic application of saliency maps to explain the decisions of convolutional neural networks for glaucoma diagnosis based on disc and cup geometry.

This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simp...

Advancements and Prospects in Nanorobotic Applications for Ophthalmic Therapy.

This study provides a bibliometric and bibliographic review of emerging applications of micro- and nanotechnology in treating ocular diseases, with a ...

Glaucoma detection and staging from visual field images using machine learning techniques.

PURPOSE: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (V...

Validation of a Visual Field Prediction Tool for Glaucoma: A Multicenter Study Involving Patients With Glaucoma in the United Kingdom.

PURPOSE: A previously developed machine-learning approach with Kalman filtering technology accurately predicted the disease trajectory for patients wi...

Comparison of an AI-based mobile pupillometry system and NPi-200 for pupillary light reflex and correlation with glaucoma-related markers.

INTRODUCTION: Glaucoma is a leading cause of blindness, often progressing asymptomatically until significant vision loss occurs. Early detection is cr...