Glaucoma

Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.

Glaucoma is a leading cause of irreversible blindness, with challenges persisting in early diagnosis...

High-Accuracy Digitization of Humphrey Visual Field Reports Using Convolutional Neural Networks.

PURPOSE: Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise visu...

Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.

PURPOSE: The purpose of this study was to develop models that predict which patients with glaucoma w...

Artificial Intelligence for Optical Coherence Tomography in Glaucoma.

PURPOSE: The integration of artificial intelligence (AI), particularly deep learning (DL), with opti...

Leveraging molecular-QTL co-association to predict novel disease-associated genetic loci using a graph convolutional neural network.

Genome-wide association studies (GWAS) have successfully uncovered numerous associations between gen...

[Primary angle closure suspects: application of machine learning method for substantiation of close monitoring].

UNLABELLED: One of the priority areas in healthcare is the concept of predictive, preventive and per...

Identification and validation of oxidative stress-related genes in primary open-angle glaucoma by weighted gene co-expression network analysis and machine learning.

Primary open-angle glaucoma (POAG) is a common ocular disease, and there is currently no effective t...

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 bio...

Long-Term Rate of Optic Disc Rim Loss in Glaucoma Patients Measured From Optic Disc Photographs With a Deep Neural Network.

PURPOSE: This study uses deep neural network-generated rim-to-disc area ratio (RADAR) measurements a...

Beyond PhacoTrainer: Deep Learning for Enhanced Trabecular Meshwork Detection in MIGS Videos.

PURPOSE: The purpose of this study was to develop deep learning models for surgical video analysis, ...

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...

How Interoperability Can Enable Artificial Intelligence in Clinical Applications.

This paper explores the critical role of Interoperability (IOP) in the integration of Artificial Int...

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...

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-ce...

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

Comment on: 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 hi...

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 t...

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. Cu...

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...

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 tr...