AIMC Topic: Fundus Oculi

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From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.

Ophthalmology Dec 20, 2018
PURPOSE: Previous approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative s...
Aged Aged, 80 and over Cross-Sectional Studies Intraocular Pressure Humans Algorithms Female ROC Curve Visual Acuity Tomography, Optical Coherence Fundus Oculi Male Middle Aged Deep Learning Glaucoma, Open-Angle Tonometry, Ocular Gonioscopy Visual Fields Visual Field Tests Optic Nerve Diseases Photography Nerve Fibers Optic Disk Retinal Ganglion Cells
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Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage.

Bioscience reports Dec 7, 2018
There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric ophthalmologists. Assistant tools that can automatically detect ocular disorders may be needed. In ...
Humans Neural Networks, Computer Fundus Oculi Retina Hemorrhage Retinal Diseases Infant, Newborn Cross-Sectional Studies Image Processing, Computer-Assisted
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Weakly Supervised Lesion Detection From Fundus Images.

IEEE transactions on medical imaging Dec 6, 2018
Early diagnosis and continuous monitoring of patients suffering from eye diseases have been major concerns in the computer-aided detection techniques. Detecting one or several specific types of retinal lesions has made a significant breakthrough in c...
Diagnostic Techniques, Ophthalmological Normal Distribution Supervised Machine Learning Image Interpretation, Computer-Assisted Retinal Diseases Fundus Oculi Retina Humans Algorithms
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Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques.

Computers in biology and medicine Dec 5, 2018
BACKGROUND AND OBJECTIVE: Glaucoma is a ocular disorder which causes irreversible damage to the retinal nerve fibers. The diagnosis of glaucoma is important as it may help to slow down the progression. The available clinical methods and imaging techn...
Humans Fundus Oculi Image Interpretation, Computer-Assisted Glaucoma Support Vector Machine
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Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity.

AMIA ... Annual Symposium proceedings. AMIA Symposium Dec 5, 2018
Accurate image-based medical diagnosis relies upon adequate image quality and clarity. This has important implications for clinical diagnosis, and for emerging methods such as telemedicine and computer-based image analysis. In this study, we trained ...
ROC Curve Infant, Newborn Area Under Curve Reproducibility of Results Fundus Oculi Deep Learning Infant, Premature Humans Retinopathy of Prematurity Neural Networks, Computer Retina Algorithms Telemedicine
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A deep learning model for the detection of both advanced and early glaucoma using fundus photography.

PloS one Nov 27, 2018
PURPOSE: To build a deep learning model to diagnose glaucoma using fundus photography.
Disease Progression Deep Learning ROC Curve Logistic Models Humans Glaucoma Cross-Sectional Studies Fundus Oculi Image Interpretation, Computer-Assisted Diagnostic Techniques, Ophthalmological
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Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images.

Clinical & experimental ophthalmology Nov 15, 2018
IMPORTANCE: Artificial intelligence (AI) algorithms are under development for use in diabetic retinopathy photo screening pathways. To be clinically acceptable, such systems must also be able to classify other fundus abnormalities and clinical featur...
Macular Degeneration Photography Area Under Curve Fundus Oculi Retina Macular Edema Reproducibility of Results Sensitivity and Specificity ROC Curve Humans Diabetic Retinopathy Databases, Factual Retrospective Studies Glaucoma Artificial Intelligence
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Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy.

Eye (London, England) Nov 6, 2018
Remarkable advances in biomedical research have led to the generation of large amounts of data. Using artificial intelligence, it has become possible to extract meaningful information from large volumes of data, in a shorter frame of time, with very ...
Deep Learning ROC Curve Fundus Oculi Diagnostic Techniques, Ophthalmological Photography Humans Diabetic Retinopathy Neural Networks, Computer Algorithms
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Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms.

BMC ophthalmology Nov 6, 2018
BACKGROUND: Convolution neural networks have been considered for automatic analysis of fundus images to detect signs of diabetic retinopathy but suffer from low sensitivity.
Humans Diabetic Retinopathy Microaneurysm Image Interpretation, Computer-Assisted Exudates and Transudates Diagnostic Techniques, Ophthalmological Retinal Hemorrhage Neural Networks, Computer Sensitivity and Specificity Fundus Oculi
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Exudate detection in fundus images using deeply-learnable features.

Computers in biology and medicine Nov 3, 2018
Presence of exudates on a retina is an early sign of diabetic retinopathy, and automatic detection of these can improve the diagnosis of the disease. Convolutional Neural Networks (CNNs) have been used for automatic exudate detection, but with poor p...
Image Processing, Computer-Assisted Fundus Oculi Tomography, Optical Humans Diabetic Retinopathy Databases, Factual Deep Learning
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