AIMC Topic: Visual Fields
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Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning.
Translational vision science & technology
Aug 1, 2022
PURPOSE: Standard automated perimetry is the gold standard to monitor visual field (VF) loss in glaucoma management, but it is prone to intrasubject variability. We trained and validated a customized deep learning (DL) regression model with Xception ...
Learning spectral initialization for phase retrieval via deep neural networks.
Applied optics
Mar 20, 2022
Phase retrieval (PR) arises from the lack of phase information in the measures recorded by optical sensors. Phase masks that modulate the optical field and reduce ambiguities in the PR problem by producing redundancy in coded diffraction patterns (CD...
Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning.
Investigative ophthalmology & visual science
Feb 1, 2022
PURPOSE: Luminance contrast is the fundamental building block of human spatial vision. Therefore contrast sensitivity, the reciprocal of contrast threshold required for target detection, has been a barometer of human visual function. Although retinal...
Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field.
Translational vision science & technology
Nov 1, 2021
PURPOSE: To investigate whether a correction based on a Humphrey field analyzer (HFA) 24-2/30-2 visual field (VF) can improve the prediction performance of a deep learning model to predict the HFA 10-2 VF test from macular optical coherence tomograph...
Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest.
Translational vision science & technology
Jul 1, 2021
PURPOSE: To compare change over time in eye-specific optical coherence tomography (OCT) retinal nerve fiber layer (RNFL)-based region-of-interest (ROI) maps developed using unsupervised deep-learning auto-encoders (DL-AE) to circumpapillary RNFL (cpR...
Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices.
Translational vision science & technology
Jun 1, 2021
PURPOSE: To develop a deep learning model to estimate the visual field (VF) from spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) and to compare the performance between them.
Diagnosing Glaucoma With Spectral-Domain Optical Coherence Tomography Using Deep Learning Classifier.
Journal of glaucoma
Apr 1, 2020
UNLABELLED: PRéCIS:: A spectral-domain optical coherence tomography (SD-OCT) based deep learning system detected glaucomatous structural change with high sensitivity and specificity. It outperformed the clinical diagnostic parameters in discriminatin...
Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans.
JAMA ophthalmology
Apr 1, 2020
IMPORTANCE: Conventional segmentation of the retinal nerve fiber layer (RNFL) is prone to errors that may affect the accuracy of spectral-domain optical coherence tomography (SD-OCT) scans in detecting glaucomatous damage.
Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence.
JAMA ophthalmology
Feb 1, 2020
IMPORTANCE: Although the central visual field (VF) in end-stage glaucoma may substantially vary among patients, structure-function studies and quality-of-life assessments are impeded by the lack of appropriate characterization of end-stage VF loss.
Accuracy of Kalman Filtering in Forecasting Visual Field and Intraocular Pressure Trajectory in Patients With Ocular Hypertension.
JAMA ophthalmology
Dec 1, 2019
IMPORTANCE: Techniques that properly identify patients in whom ocular hypertension (OHTN) is likely to progress to open-angle glaucoma can assist clinicians with deciding on the frequency of monitoring and the potential benefit of early treatment.
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