AIMC Topic: Wet Macular Degeneration

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Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning.

JAMA ophthalmology Sep 1, 2021
IMPORTANCE: Quantitative volumetric measures of retinal disease in optical coherence tomography (OCT) scans are infeasible to perform owing to the time required for manual grading. Expert-level deep learning systems for automatic OCT segmentation hav...
Humans Diabetic Retinopathy Female Tomography, Optical Coherence Macular Edema Wet Macular Degeneration Male Deep Learning
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Model-to-Data Approach for Deep Learning in Optical Coherence Tomography Intraretinal Fluid Segmentation.

JAMA ophthalmology Oct 1, 2020
IMPORTANCE: Amid an explosion of interest in deep learning in medicine, including within ophthalmology, concerns regarding data privacy, security, and sharing are of increasing importance. A model-to-data approach, in which the model itself is transf...
Tomography, Optical Coherence Wet Macular Degeneration Subretinal Fluid Male Deep Learning Algorithms Female Aged Cross-Sectional Studies Humans Reproducibility of Results Neural Networks, Computer
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SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY, FLUORESCEIN ANGIOGRAPHY, AND INDOCYANINE GREEN ANGIOGRAPHY COMPARISONS REVISITED: Using a Novel Deep-Learning-Assisted Approach for Image Registration.

Retina (Philadelphia, Pa.) Oct 1, 2020
PURPOSE: To compare area measurements between swept source optical coherence tomography angiography (SSOCTA), fluorescein angiography (FA), and indocyanine green angiography (ICGA) after applying a novel deep-learning-assisted algorithm for accurate ...
Female Reproducibility of Results Aged Humans Prospective Studies Wet Macular Degeneration Choroidal Neovascularization Visual Acuity Tomography, Optical Coherence Indocyanine Green Fluorescein Angiography Coloring Agents Middle Aged Aged, 80 and over Diagnosis, Computer-Assisted Male Deep Learning
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Deep Learning for Prediction of AMD Progression: A Pilot Study.

Investigative ophthalmology & visual science Feb 1, 2019
PURPOSE: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning.
Tomography, Optical Coherence Wet Macular Degeneration Diagnosis, Computer-Assisted Deep Learning Humans ROC Curve Machine Learning Follow-Up Studies Male Female Pilot Projects Middle Aged Aged Neural Networks, Computer Disease Progression Aged, 80 and over
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Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence.

Investigative ophthalmology & visual science Jul 2, 2018
PURPOSE: While millions of individuals show early age-related macular degeneration (AMD) signs, yet have excellent vision, the risk of progression to advanced AMD with legal blindness is highly variable. We suggest means of artificial intelligence to...
Prospective Studies Female Geographic Atrophy Aged Artificial Intelligence Machine Learning Wet Macular Degeneration Middle Aged Retinal Drusen Disease Progression Choroidal Neovascularization Double-Blind Method Tomography, Optical Coherence Aged, 80 and over Image Interpretation, Computer-Assisted Male Humans
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Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.

JAMA ophthalmology Nov 1, 2017
IMPORTANCE: Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying indi...
Reproducibility of Results ROC Curve Wet Macular Degeneration Machine Learning Humans Fundus Oculi Algorithms Neural Networks, Computer
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