Ophthalmology

Glaucoma

Latest AI and machine learning research in glaucoma for healthcare professionals.

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Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks.

Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diag...

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive am...

Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model.

We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular gang...

A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans.

PURPOSE: The purpose of this study was to develop a 3D deep learning system from spectral domain opt...

Improving the Structure-Function Relationship in Glaucomatous Visual Fields by Using a Deep Learning-Based Noise Reduction Approach.

PURPOSE: To investigate whether processing visual field (VF) measurements using a variational autoen...

Development and validation of a machine learning, smartphone-based tonometer.

BACKGROUND/AIMS: To compare intraocular pressure (IOP) measurements using a prototype smartphone ton...

Machine learning classifiers-based prediction of normal-tension glaucoma progression in young myopic patients.

PURPOSE: To assess the performance of machine learning classifiers for prediction of progression of ...

Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma.

PURPOSE: To quantify the central visual field (VF) loss patterns in glaucoma using artificial intell...

[Artificial Intelligence for the Development of Screening Parameters in the Field of Corneal Biomechanics].

Machine learning and artificial intelligence are mostly important if data analysis by knowledge-base...

Ophthalmic diagnosis using deep learning with fundus images - A critical review.

An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus image...

Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs.

PURPOSE: To compare the diagnostic performance of human gradings vs predictions provided by a machin...

Forecasting Retinal Nerve Fiber Layer Thickness from Multimodal Temporal Data Incorporating OCT Volumes.

PURPOSE: The purpose of this study was to develop a machine learning model to forecast future circum...

Multi-indices quantification of optic nerve head in fundus image via multitask collaborative learning.

Multi-indices quantification of optic nerve head (ONH), measuring ONH appearance with multiple types...

Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis.

Despite the potential to revolutionise disease diagnosis by performing data-driven classification, c...

Glaucoma management in the era of artificial intelligence.

Glaucoma is a result of irreversible damage to the retinal ganglion cells. While an early interventi...

Joint optic disc and cup segmentation using semi-supervised conditional GANs.

Glaucoma is a chronic and widespread eye disease threatening humans' irreversible vision loss. The c...

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs.

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age popul...

Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.

PURPOSE: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous...

The impact of artificial intelligence in the diagnosis and management of glaucoma.

Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural network...

Machine Learning Models for Diagnosing Glaucoma from Retinal Nerve Fiber Layer Thickness Maps.

PURPOSE: To assess the diagnostic accuracy of multiple machine learning models using full retinal ne...

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