Glaucoma

SMOTE-Enhanced Explainable Artificial Intelligence Model for Predicting Visual Field Progression in Myopic Normal Tension Glaucoma.

PRCIS: The AI model, enhanced by SMOTE to balance data classes, accurately predicted visual field deterioration in patients with myopic normal tension glaucoma. Using SHAP analysis, the key variables driving disease progression were identified.

Two-stage color fundus image registration via Keypoint Refinement and Confidence-Guided Estimation.

Color fundus images are widely used for diagnosing diseases such as Glaucoma, Cataracts, and Diabetic Retinopathy. The registration of color fundus images is crucial for assessing changes in fundus appearance to determine disease progression. In this paper, a novel two-stage framework is proposed for conducting end-to-end color fundus image registration without requiring any training or annotation...

Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model.

A segmentation-free 3D Convolutional Neural Network (3DCNN) model was adopted to estimate Visual Field (VF) in glaucoma cases using Optical Coherence ...

Influence of artificial intelligence on ophthalmologists' judgments in glaucoma.

PURPOSE: To examine the influence of artificial intelligence (AI) on physicians' judgments regarding the presence and severity of glaucoma on fundus p...

Grey wolf optimization technique with U-shaped and capsule networks-A novel framework for glaucoma diagnosis.

The worldwide prevalence of glaucoma makes it a major reason for blindness thus proper early diagnosis remains essential for preventing major vision d...

Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method.

Glaucoma is the leading cause of irreversible vision impairment, emphasizing the critical need for early detection. Typically, AI-based glaucoma scree...

PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data.

: Glaucoma (GL) classification is crucial for early diagnosis and treatment, yet relying solely on stand-alone models or International Classification ...

Application of machine learning techniques in GlaucomAI system for glaucoma diagnosis and collaborative research support.

This paper proposes an architecture of the system that provides support for collaborative research focused on analysis of data acquired using Triggerf...

Deep Learning-Driven Glaucoma Medication Bottle Recognition: A Multilingual Clinical Validation Study in Patients with Impaired Vision.

OBJECTIVE: To clinically validate a convolutional neural network (CNN)-based Android smartphone app in the identification of topical glaucoma medicati...

Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence.

The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy,...

Comparison of Deep Learning and Clinician Performance for Detecting Referable Glaucoma from Fundus Photographs in a Safety Net Population.

PURPOSE: Develop and test a deep learning (DL) algorithm for detecting referable 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 glauc...

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 su...

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...