AIMC Topic: Tomography, Optical Coherence
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Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography.
PloS one
Apr 16, 2020
This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular...
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images.
Translational vision science & technology
Apr 15, 2020
PURPOSE: To remove blood vessel shadows from optical coherence tomography (OCT) images of the optic nerve head (ONH).
Ensemble Deep Learning for Diabetic Retinopathy Detection Using Optical Coherence Tomography Angiography.
Translational vision science & technology
Apr 13, 2020
PURPOSE: To evaluate the role of ensemble learning techniques with deep learning in classifying diabetic retinopathy (DR) in optical coherence tomography angiography (OCTA) images and their corresponding co-registered structural images.
Determinants of Cone and Rod Functions in Geographic Atrophy: AI-Based Structure-Function Correlation.
American journal of ophthalmology
Apr 11, 2020
PURPOSE: To investigate the association between retinal microstructure and cone and rod function in geographic atrophy (GA) secondary to age-related macular degeneration (AMD) by using artificial intelligence (AI) algorithms.
Role of Deep Learning-Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression.
American journal of ophthalmology
Apr 8, 2020
PURPOSE: To quantitatively measure hyperreflective foci (HRF) during the progression of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) using deep learning (DL) and investigate the association with local and global growth ...
Artificial Intelligence Mapping of Structure to Function in Glaucoma.
Translational vision science & technology
Mar 30, 2020
PURPOSE: To develop an artificial intelligence (AI)-based structure-function (SF) map relating retinal nerve fiber layer (RNFL) damage on spectral domain optical coherence tomography (SDOCT) to functional loss on standard automated perimetry (SAP).
A Deep-Learning Approach for Automated OCT En-Face Retinal Vessel Segmentation in Cases of Optic Disc Swelling Using Multiple En-Face Images as Input.
Translational vision science & technology
Mar 24, 2020
PURPOSE: In cases of optic disc swelling, segmentation of projected retinal blood vessels from optical coherence tomography (OCT) volumes is challenging due to swelling-based shadowing artifacts. Based on our hypothesis that simultaneously considerin...
Classification of optical coherence tomography images using a capsule network.
BMC ophthalmology
Mar 19, 2020
BACKGROUND: Classification of optical coherence tomography (OCT) images can be achieved with high accuracy using classical convolution neural networks (CNN), a commonly used deep learning network for computer-aided diagnosis. Classical CNN has often ...
Application of a Deep Machine Learning Model for Automatic Measurement of EZ Width in SD-OCT Images of RP.
Translational vision science & technology
Mar 17, 2020
PURPOSE: We applied a deep convolutional neural network model for automatic identification of ellipsoid zone (EZ) in spectral domain optical coherence tomography B-scans of retinitis pigmentosa (RP).
Diabetic retinopathy and ultrawide field imaging.
Seminars in ophthalmology
Mar 13, 2020
The introduction of ultrawide field imaging has allowed the visualization of approximately 82% of the total retinal area compared to only 30% using 7-standard field Early Treatment Diabetic Retinopathy (ETDRS) photography. This substantially wider fi...
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