Glaucoma

Using Multi-Layer Perceptron Driven Diagnosis to Compare Biomarkers for Primary Open Angle Glaucoma.

PURPOSE: To use neural network machine learning (ML) models to identify the most relevant ocular biomarkers for the diagnosis of primary open-angle glaucoma (POAG).

Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images.

OBJECTIVE: We developed an optimized decision support system for retinal fundus image-based glaucoma screening.

How Interoperability Can Enable Artificial Intelligence in Clinical Applications.

This paper explores the critical role of Interoperability (IOP) in the integration of Artificial Intelligence (AI) for clinical applications. As AI ga...

Deep Learning to Predict Glaucoma Progression using Structural Changes in the Eye

Glaucoma is a chronic eye disease characterized by optic neuropathy, leading to irreversible vision loss. It progresses gradually, often remaining u...

A Hybrid Deep Learning Classification of Perimetric Glaucoma Using Peripapillary Nerve Fiber Layer Reflectance and Other OCT Parameters from Three Anatomy Regions

Precis: A hybrid deep-learning model combines NFL reflectance and other OCT parameters to improve glaucoma diagnosis. Objective: To investigate if a...

Visual Field Prognosis From Macula and Circumpapillary Spectral Domain Optical Coherence Tomography.

PURPOSE: To explore the structural-functional loss relationship from optic-nerve-head- and macula-centred spectral-domain (SD) Optical Coherence Tomog...

Predicting Glaucoma Surgical Outcomes Using Neural Networks and Machine Learning on Electronic Health Records.

PURPOSE: To develop machine learning (ML) and deep learning (DL) models to predict glaucoma surgical outcomes, including postoperative intraocular pre...

Comment on: Artificial intelligence chatbot and Academy Preferred Practice Pattern ® Guidelines on cataract and glaucoma.

Reply: Artificial intelligence chatbot and Academy Preferred Practice Pattern® Guidelines on cataract and glaucoma.

Quantitative Assessment of Fundus Tessellated Density in Highly Myopic Glaucoma Using Deep Learning.

PURPOSE: To characterize the fundus tessellated density (FTD) in highly myopic glaucoma (HMG) and high myopia (HM) for discovering early signs and dia...

A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection.

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio ...

Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.

OBJECTIVE: Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and pos...

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

PURPOSE: To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated PO...

A neural network model for predicting the effectiveness of treatment in patients with neovascular glaucoma associated with diabetes mellitus.

INTRODUCTION: The study hypothesizes that neural networks can be an effective tool for predicting treatment outcomes in patients with diabetic neovasc...

Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models.

AIMS: The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms.

Deep Learning-based Glaucoma Detection Using CNN and Digital Fundus Images: A Promising Approach for Precise Diagnosis.

BACKGROUND: Glaucoma is a significant cause of irreversible blindness worldwide, with symptoms often going undetected until the patient's visual field...

[Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis].

This article reviews literature on the use of artificial intelligence (AI) for screening, diagnosis, monitoring and treatment of glaucoma. The first p...

Multi-dimensional dense attention network for pixel-wise segmentation of optic disc in colour fundus images.

BACKGROUND: Segmentation of retinal fragments like blood vessels, Optic Disc (OD), and Optic Cup (OC) enables the early detection of different retinal...

[Application of artificial intelligence in glaucoma. Part 2. Neural networks and machine learning in the monitoring and treatment of glaucoma].

The second part of the literature review on the application of artificial intelligence (AI) methods for screening, diagnosing, monitoring, and treatin...

Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data.

PURPOSE: This study aims to investigate generalizability of deep learning (DL) models trained on commonly used public fundus images to an instance of ...