AIMC Topic: Retinal Drusen
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Predicting Progression to Advanced Age-Related Macular Degeneration from Clinical, Genetic, and Lifestyle Factors Using Machine Learning.
Ophthalmology
Sep 2, 2020
PURPOSE: Current prediction models for advanced age-related macular degeneration (AMD) are based on a restrictive set of risk factors. The objective of this study was to develop a comprehensive prediction model applying a machine learning algorithm a...
Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2.
Ophthalmology
May 21, 2020
PURPOSE: To develop deep learning models for detecting reticular pseudodrusen (RPD) using fundus autofluorescence (FAF) images or, alternatively, color fundus photographs (CFP) in the context of age-related macular degeneration (AMD).
Automated detection and classification of early AMD biomarkers using deep learning.
Scientific reports
Jul 29, 2019
Age-related macular degeneration (AMD) affects millions of people and is a leading cause of blindness throughout the world. Ideally, affected individuals would be identified at an early stage before late sequelae such as outer retinal atrophy or exud...
DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs.
Ophthalmology
Nov 22, 2018
PURPOSE: In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming partic...
Diagnostic Techniques, Ophthalmological
Photography
Retinal Drusen
Risk Factors
Geographic Atrophy
Aged
Aged, 80 and over
Sensitivity and Specificity
Diagnosis, Computer-Assisted
Deep Learning
Models, Theoretical
Humans
Male
Area Under Curve
Prospective Studies
Female
Middle Aged
Reproducibility of Results
Disease Progression
Severity of Illness Index
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).
Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep Learning.
Investigative ophthalmology & visual science
Jul 1, 2024
PURPOSE: Investigating the sequence of morphological changes preceding outer plexiform layer (OPL) subsidence, a marker preceding geographic atrophy, in intermediate AMD (iAMD) using high-precision artificial intelligence (AI) quantifications on opti...
A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography.
Translational vision science & technology
Dec 1, 2022
PURPOSE: The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans.
A SYSTEMATIC REVIEW OF DEEP LEARNING APPLICATIONS FOR OPTICAL COHERENCE TOMOGRAPHY IN AGE-RELATED MACULAR DEGENERATION.
Retina (Philadelphia, Pa.)
Aug 1, 2022
PURPOSE: To survey the current literature regarding applications of deep learning to optical coherence tomography in age-related macular degeneration (AMD).
Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration.
Journal of the American Medical Informatics Association : JAMIA
Jun 12, 2021
OBJECTIVE: Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and typically require advanced imaging modalities such as fundus autoflu...
Multiclass retinal disease classification and lesion segmentation in OCT B-scan images using cascaded convolutional networks.
Applied optics
Nov 20, 2020
Disease classification and lesion segmentation of retinal optical coherence tomography images play important roles in ophthalmic computer-aided diagnosis. However, existing methods achieve the two tasks separately, which is insufficient for clinical ...
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