BACKGROUND: This study is to evaluate the accuracy of machine learning for differentiation between optic neuropathies, pseudopapilledema (PPE) and normals.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
We describe and assess convolutional neural network (CNN) models for detection of glaucoma based upon optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability maps. CNNs pretrained on natural images performed comparably to CNNs...
PURPOSE: To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT)...
PURPOSE: Previous approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative s...
PURPOSE: We sought to construct and evaluate a deep learning (DL) model to diagnose early glaucoma from spectral-domain optical coherence tomography (OCT) images.
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 evaluate the relationship between macular vessel density and ganglion cell to inner plexiform layer thickness (GCIPLT) and to compare their diagnostic performance. We attempted to develop a new combined parameter using an artificial neura...
Investigative ophthalmology & visual science
Jun 1, 2018
PURPOSE: To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progres...