AIMC Topic: Macula Lutea
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Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques.
PloS one
May 6, 2019
OBJECTIVE: To compare axonal loss in ganglion cells detected with swept-source optical coherence tomography (SS-OCT) in eyes of patients with multiple sclerosis (MS) versus healthy controls using different machine learning techniques. To analyze the ...
Feasibility of support vector machine learning in age-related macular degeneration using small sample yielding sparse optical coherence tomography data.
Acta ophthalmologica
Mar 6, 2019
PURPOSE: A retrospective pilot study is conducted to demonstrate the utility of a novel support vector machine learning (SVML) algorithm in a small three-dimensional (3D) sample yielding sparse optical coherence tomography (spOCT) data for the automa...
Male
Middle Aged
Disease Progression
Pilot Projects
Retrospective Studies
Female
Aged
Aged, 80 and over
Follow-Up Studies
Tomography, Optical Coherence
Time Factors
Feasibility Studies
Humans
ROC Curve
Algorithms
Macula Lutea
Imaging, Three-Dimensional
Prognosis
Wet Macular Degeneration
Support Vector Machine
Development of an efficient algorithm for the detection of macular edema from optical coherence tomography images.
International journal of computer assisted radiology and surgery
May 29, 2018
PURPOSE: Detection of eye diseases and their treatment is a key to reduce blindness, which impacts human daily needs like driving, reading, writing, etc. Several methods based on image processing have been used to monitor the presence of macular dise...
A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography.
Ophthalmology
Apr 10, 2018
PURPOSE: Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these...
Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Nov 20, 2017
PURPOSE: Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT).
Use of a Neural Net to Model the Impact of Optical Coherence Tomography Abnormalities on Vision in Age-related Macular Degeneration.
American journal of ophthalmology
Oct 31, 2017
PURPOSE: To develop a neural network for the estimation of visual acuity from optical coherence tomography (OCT) images of patients with neovascular age-related macular degeneration (AMD) and to demonstrate its use to model the impact of specific con...
Diabetic retinopathy screening using deep neural network.
Clinical & experimental ophthalmology
Oct 4, 2017
IMPORTANCE: There is a burgeoning interest in the use of deep neural network in diabetic retinal screening.
Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning.
Scientific reports
Jun 7, 2017
Vitreomacular adhesion (VMA) represents a prognostic biomarker in the management of exudative macular disease using anti-vascular endothelial growth factor (VEGF) agents. However, manual evaluation of VMA in 3D optical coherence tomography (OCT) is l...
Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration.
Ophthalmology. Retina
May 31, 2017
PURPOSE: To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascul...
Retinal Pigment Epithelium
Wet Macular Degeneration
Visual Acuity
Tomography, Optical Coherence
Intravitreal Injections
Humans
Reproducibility of Results
Fluorescein Angiography
Fundus Oculi
Machine Learning
Angiogenesis Inhibitors
Macula Lutea
Vascular Endothelial Growth Factor A
Prospective Studies
Treatment Outcome
Ranibizumab
Prognosis
Follow-Up Studies
Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach.
BMC ophthalmology
Jan 27, 2015
BACKGROUND: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD).
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