AIMC Topic: Fundus Oculi

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A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features.

PloS one Mar 6, 2020
This paper proposes a new supervised method for blood vessel segmentation using Zernike moment-based shape descriptors. The method implements a pixel wise classification by computing a 11-D feature vector comprising of both statistical (gray-level) f...
Image Processing, Computer-Assisted Fundus Oculi Supervised Machine Learning Retinal Vessels Humans
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Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model.

Scientific reports Feb 24, 2020
We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and sp...
Case-Control Studies Diagnosis, Computer-Assisted Deep Learning ROC Curve Sensitivity and Specificity Intraocular Pressure Glaucoma Humans Male Aged Fundus Oculi Middle Aged Support Vector Machine Aged, 80 and over Translational Research, Biomedical Regression Analysis Algorithms Retinal Ganglion Cells Young Adult Optic Nerve Adult Diagnostic Techniques, Ophthalmological Retrospective Studies Tomography, Optical Coherence Refractometry Female
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A Deep Learning Model for Segmentation of Geographic Atrophy to Study Its Long-Term Natural History.

Ophthalmology Feb 15, 2020
PURPOSE: To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA.
Prospective Studies Female Aged Male Disease Progression Humans Forecasting Retina Fluorescein Angiography Severity of Illness Index Geographic Atrophy Deep Learning Follow-Up Studies Fundus Oculi
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Domain-invariant interpretable fundus image quality assessment.

Medical image analysis Jan 30, 2020
Objective and quantitative assessment of fundus image quality is essential for the diagnosis of retinal diseases. The major factors in fundus image quality assessment are image artifact, clarity, and field definition. Unfortunately, most of existing ...
Artifacts Datasets as Topic Optic Disk Retinal Diseases Photography Humans Machine Learning Fundus Oculi Image Interpretation, Computer-Assisted
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Factors in Color Fundus Photographs That Can Be Used by Humans to Determine Sex of Individuals.

Translational vision science & technology Jan 30, 2020
PURPOSE: Artificial intelligence (AI) can identify the sex of an individual from color fundus photographs (CFPs). However, the mechanism(s) involved in this identification has not been determined. This study was conducted to determine the information...
Artificial Intelligence Female Adult Cross-Sectional Studies Male Tomography, Optical Coherence Fundus Oculi Optic Disk Humans
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Development and Evaluation of a Deep Learning System for Screening Retinal Hemorrhage Based on Ultra-Widefield Fundus Images.

Translational vision science & technology Jan 29, 2020
PURPOSE: To develop and evaluate a deep learning (DL) system for retinal hemorrhage (RH) screening using ultra-widefield fundus (UWF) images.
Deep Learning Adolescent Child, Preschool Child Retinal Diseases Retinal Hemorrhage Young Adult Fundus Oculi Retina Adult Humans Male Female Middle Aged Aged Aged, 80 and over
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Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Jan 14, 2020
PURPOSE: To automatically detect and classify the lesions of diabetic retinopathy (DR) in fundus fluorescein angiography (FFA) images using deep learning algorithm through comparing 3 convolutional neural networks (CNNs).
Middle Aged Deep Learning Image Processing, Computer-Assisted Fundus Oculi Algorithms Female Aged Male Retina Fluorescein Angiography Humans Diabetic Retinopathy Neural Networks, Computer
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Ophthalmic diagnosis using deep learning with fundus images - A critical review.

Artificial intelligence in medicine Nov 22, 2019
An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented. We describe various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation...
Deep Learning Eye Diseases Humans Diagnostic Techniques, Ophthalmological Ophthalmology Eye Fundus Oculi Image Interpretation, Computer-Assisted
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A multi-context CNN ensemble for small lesion detection.

Artificial intelligence in medicine Nov 13, 2019
In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial con...
Humans Neural Networks, Computer Breast Neoplasms Deep Learning Image Processing, Computer-Assisted Mammography Fundus Oculi
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Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs.

American journal of ophthalmology Nov 12, 2019
PURPOSE: To compare the diagnostic performance of human gradings vs predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm trained to quantify retinal nerve fiber layer (RNFL) damage on fundus photographs.
Physical Examination Glaucoma, Open-Angle Gonioscopy Visual Fields Cross-Sectional Studies Visual Field Tests Tomography, Optical Coherence Intraocular Pressure Optic Nerve Diseases Humans Fundus Oculi Male Photography Algorithms Deep Learning Nerve Fibers Middle Aged Retrospective Studies ROC Curve Female Retinal Ganglion Cells Vision Disorders Aged Area Under Curve
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