AIMC Topic: Intraocular Pressure
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Detection of glaucoma progression on longitudinal series of en-face macular optical coherence tomography angiography images with a deep learning model.
The British journal of ophthalmology
Nov 22, 2024
BACKGROUND/AIMS: To design a deep learning (DL) model for the detection of glaucoma progression with a longitudinal series of macular optical coherence tomography angiography (OCTA) images.
Intraocular Pressure
Male
Middle Aged
Disease Progression
Deep Learning
Humans
Retrospective Studies
Female
Aged
Visual Fields
Visual Field Tests
Fluorescein Angiography
Macula Lutea
Retinal Vessels
Glaucoma, Open-Angle
ROC Curve
Follow-Up Studies
Tomography, Optical Coherence
Optic Disk
Retinal Ganglion Cells
Diagnostic Performance of the Offline Medios Artificial Intelligence for Glaucoma Detection in a Rural Tele-Ophthalmology Setting.
Ophthalmology. Glaucoma
Sep 12, 2024
PURPOSE: This study assesses the diagnostic efficacy of offline Medios Artificial Intelligence (AI) glaucoma software in a primary eye care setting, using nonmydriatic fundus images from Remidio's Fundus-on-Phone (FOP NM-10). Artificial intelligence ...
Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.
Journal of glaucoma
Aug 5, 2024
PRCIS: We developed unsupervised machine learning models to identify different subtypes of patients with ocular hypertension in terms of visual field (VF) progression and discovered 4 subtypes with different trends of VF worsening. We then identified...
Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning.
The British journal of ophthalmology
Jul 23, 2024
BACKGROUND: Deep learning (DL) is promising to detect glaucoma. However, patients' privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model using the federated learning (FL) ...
Prediction of visual field progression with serial optic disc photographs using deep learning.
The British journal of ophthalmology
Jul 23, 2024
AIM: We tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up.
Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects.
The British journal of ophthalmology
Jun 20, 2024
BACKGROUND/AIMS: To assess the performance of deep-learning (DL) models for prediction of conversion to normal-tension glaucoma (NTG) in normotensive glaucoma suspect (GS) patients.
Evaluation of machine learning approach for surgical results of Ahmed valve implantation in patients with glaucoma.
BMC ophthalmology
Jun 11, 2024
BACKGROUND: Ahmed valve implantation demonstrated an increasing proportion in glaucoma surgery, but predicting the successful maintenance of target intraocular pressure remains a challenging task. This study aimed to evaluate the performance of machi...
Artificial Intelligence in Anterior Chamber Evaluation: A Systematic Review and Meta-Analysis.
Journal of glaucoma
May 16, 2024
PRCIS: In this meta-analysis of 6 studies and 5269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed angle compared with gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6...
Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression From Age-Related Changes in OCT Scans.
American journal of ophthalmology
May 3, 2024
PURPOSE: To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard.
Optic Disk
Retinal Ganglion Cells
Visual Fields
Optic Nerve Diseases
Glaucoma, Open-Angle
Neural Networks, Computer
Glaucoma
Algorithms
Disease Progression
Retrospective Studies
Deep Learning
Female
Aged
Humans
Follow-Up Studies
Intraocular Pressure
Tomography, Optical Coherence
Male
Nerve Fibers
Aging
Middle Aged
Recognition of Glaucomatous Fundus Images Using Machine Learning Methods Based on Optic Nerve Head Topographic Features.
Journal of glaucoma
Mar 29, 2024
PRCIS: Machine learning classifiers are an effective approach to detecting glaucomatous fundus images based on optic disc topographic features making it a straightforward and effective approach.
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