AIMC Topic: Intraocular Pressure
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A neural network model for predicting the effectiveness of treatment in patients with neovascular glaucoma associated with diabetes mellitus.
Romanian journal of ophthalmology
Jan 1, 2024
INTRODUCTION: The study hypothesizes that neural networks can be an effective tool for predicting treatment outcomes in patients with diabetic neovascular glaucoma (NVG), considering not only baseline intraocular pressure (IOP) values but also inflam...
Importance and use of corneal biomechanics and its diagnostic utility.
Cirugia y cirujanos
Jan 1, 2023
The study of corneal biomechanics has become relevant in recent years due to its possible applications in the diagnosis, management, and treatment of various diseases such as glaucoma, keratorefractive surgery and different corneal diseases. The clin...
Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning.
JAMA ophthalmology
Apr 1, 2022
IMPORTANCE: Automated deep learning (DL) analyses of fundus photographs potentially can reduce the cost and improve the efficiency of reading center assessment of end points in clinical trials.
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...
Deep Learning-based Diagnosis of Glaucoma Using Wide-field Optical Coherence Tomography Images.
Journal of glaucoma
Sep 1, 2021
PURPOSE: (1) To evaluate the performance of deep learning (DL) classifier in detecting glaucoma, based on wide-field swept-source optical coherence tomography (SS-OCT) images. (2) To assess the performance of DL-based fusion methods in diagnosing gla...
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.
Use of Telepresence Robots in Glaucoma Patient Education.
Journal of glaucoma
Mar 1, 2021
PRCIS: Telepresence robots (TR) present the versatility to effectively provide remote educational sessions for patients affected by glaucoma to improve disease knowledge. Given COVID-19's effect on clinical practice, TR can maintain social distancing...
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.
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