PURPOSE: To assess the performance of machine learning classifiers for prediction of progression of normal-tension glaucoma (NTG) in young myopic patients.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Dec 7, 2019
PURPOSE: To develop a deep learning (DL) model for automated detection of glaucoma and to compare diagnostic capability against hand-craft features (HCFs) based on spectral domain optical coherence tomography (SD-OCT) peripapillary retinal nerve fibe...
PURPOSE: To compare the diagnostic performance of human gradings vs predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm trained to quantify retinal nerve fiber layer (RNFL) damage on fundus photographs.
PURPOSE: The purpose of this study was to develop a machine learning model to forecast future circumpapillary retinal nerve fiber layer (cpRNFL) thickness in eyes of healthy, glaucoma suspect, and glaucoma participants from multimodal temporal data.
Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural networks modelled after the mammalian visual cortex capable of synthesizing images in ways that will transform the field of glaucoma. Autonomous DL algorithm...
PURPOSE: To assess the diagnostic accuracy of multiple machine learning models using full retinal nerve fiber layer (RNFL) thickness maps in detecting glaucoma.
PURPOSE: To develop and test deep learning classifiers that detect gonioscopic angle closure and primary angle closure disease (PACD) based on fully automated analysis of anterior segment OCT (AS-OCT) images.
PURPOSE: To predict the need for surgical intervention in patients with primary open-angle glaucoma (POAG) using systemic data in electronic health records (EHRs).
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