AIMC Topic: Fundus Oculi

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Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

Ophthalmology Mar 2, 2018
PURPOSE: To assess the performance of a deep learning algorithm for detecting referable glaucomatous optic neuropathy (GON) based on color fundus photographs.
Humans Glaucoma Algorithms Optic Disk Optic Nerve Diseases Diagnostic Techniques, Ophthalmological Photography Retrospective Studies Deep Learning ROC Curve Fundus Oculi
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Microaneurysm detection using fully convolutional neural networks.

Computer methods and programs in biomedicine Feb 22, 2018
BACKROUND AND OBJECTIVES: Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automat...
Microaneurysm Fundus Oculi Datasets as Topic Automation Photography Humans Diabetic Retinopathy Neural Networks, Computer Algorithms Diagnostic Imaging
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Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.

Nature biomedical engineering Feb 19, 2018
Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be di...
Humans Algorithms Female Fundus Oculi Retina Male Image Interpretation, Computer-Assisted Cardiovascular Diseases Middle Aged Risk Factors Deep Learning Aged Aged, 80 and over
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Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

Molecules (Basel, Switzerland) Nov 23, 2017
The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpf...
Humans Diabetic Retinopathy Neural Networks, Computer Fluorescein Angiography Algorithms Image Processing, Computer-Assisted Fundus Oculi Retina
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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...
Wet Macular Degeneration Fundus Oculi Fluorescein Angiography Macula Lutea Retrospective Studies Female Reproducibility of Results Aged Male Middle Aged Visual Acuity Tomography, Optical Coherence Humans Neural Networks, Computer
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An ensemble deep learning based approach for red lesion detection in fundus images.

Computer methods and programs in biomedicine Oct 14, 2017
BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical ...
Image Interpretation, Computer-Assisted Microaneurysm Neural Networks, Computer Machine Learning Fundus Oculi Humans Diabetic Retinopathy
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Pneumatic Displacement of Submacular Hemorrhage with Subretinal Air and Tissue Plasminogen Activator: Initial United States Experience.

Ophthalmology. Retina Sep 28, 2017
PURPOSE: To present the initial multicenter experience of using subretinal air injection in combination with tissue plasminogen activator (tPA) at the time of pars plana vitrectomy (PPV) to displace submacular hemorrhage (SMH).
Endotamponade Time Factors Vitrectomy Retina Fluorescein Angiography United States Aged Tissue Plasminogen Activator Injections, Intraocular Treatment Outcome Visual Acuity Humans Aged, 80 and over Follow-Up Studies Fibrinolytic Agents Retinal Hemorrhage Air Retrospective Studies Male Tomography, Optical Coherence Female Middle Aged Fundus Oculi
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Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.

Scientific reports Aug 25, 2017
Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep le...
Middle Aged Deep Learning ROC Curve Female Ophthalmoscopy Fundus Oculi Aged Retinal Detachment Sensitivity and Specificity Male Humans Machine Learning
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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...
Macula Lutea Ranibizumab Retinal Pigment Epithelium Wet Macular Degeneration Fundus Oculi Vascular Endothelial Growth Factor A Intravitreal Injections Fluorescein Angiography Follow-Up Studies Prognosis Visual Acuity Reproducibility of Results Humans Tomography, Optical Coherence Angiogenesis Inhibitors Machine Learning Treatment Outcome Prospective Studies
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Decision Support System for Detection of Papilledema through Fundus Retinal Images.

Journal of medical systems Mar 10, 2017
A condition in which the optic nerve inside the eye is swelled due to increased intracranial pressure is known as papilledema. The abnormalities due to papilledema such as opacification of Retinal Nerve Fiber Layer (RNFL), dilated optic disc capillar...
Humans Support Vector Machine Fundus Oculi Pakistan Image Interpretation, Computer-Assisted Papilledema
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