AIMC Topic: Macular Edema
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Linking Function and Structure with ReSensNet: Predicting Retinal Sensitivity from OCT using Deep Learning.
Ophthalmology. Retina
Feb 5, 2022
PURPOSE: The currently used measures of retinal function are limited by being subjective, nonlocalized, or taxing for patients. To address these limitations, we sought to develop and evaluate a deep learning (DL) method to automatically predict the f...
Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation Study.
Ophthalmology. Retina
Jan 5, 2022
PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from 2-dimensional color fundus photographs (CFP), for which the reference standard for retinal thickness and fluid presence is derive...
Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.
Sensors (Basel, Switzerland)
Aug 5, 2021
Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems ...
A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis.
Diabetes care
Jul 27, 2021
OBJECTIVE: Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially...
Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.
PloS one
Jul 27, 2021
An efficient automatic decision support system for detection of retinal disorders is important and is the need of the hour. Optical Coherence Tomography (OCT) is the current imaging modality for the early detection of retinal disorders non-invasively...
Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs.
Eye (London, England)
Jul 1, 2021
OBJECTIVES: To present and validate a deep ensemble algorithm to detect diabetic retinopathy (DR) and diabetic macular oedema (DMO) using retinal fundus images.
Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data.
Acta ophthalmologica
May 15, 2021
PURPOSE: To meet the demands imposed by the continuing growth of the Age-related macular degeneration (AMD) patient population, automation of follow-ups by detecting retinal oedema using deep learning might be a viable approach. However, preparing an...
Machine Learning Can Predict Anti-VEGF Treatment Demand in a Treat-and-Extend Regimen for Patients with Neovascular AMD, DME, and RVO Associated Macular Edema.
Ophthalmology. Retina
May 8, 2021
PURPOSE: To assess the potential of machine learning to predict low and high treatment demand in real life in patients with neovascular age-related macular degeneration (nAMD), retinal vein occlusion (RVO), and diabetic macular edema (DME) treated ac...
Vascular Endothelial Growth Factor A
Intravitreal Injections
Ranibizumab
Wet Macular Degeneration
Angiogenesis Inhibitors
Follow-Up Studies
Macular Edema
Retinal Vein Occlusion
Prognosis
Retrospective Studies
Female
Aged
Aged, 80 and over
Male
Middle Aged
Humans
Diabetic Retinopathy
Machine Learning
Subfoveal choroidal thickness changes after intravitreal ranibizumab injections in different patterns of diabetic macular edema using a deep learning-based auto-segmentation.
International ophthalmology
Mar 30, 2021
PURPOSE: To evaluate the effect of intravitreal injection of ranibizumab (IVR) on subfoveal choroidal thickness (SFCT) and its relationship with central macular thickness (CMT) and best-corrected visual acuity (BCVA) changes in eyes with center-invol...
Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders.
Journal of diabetes research
Dec 15, 2020
OBJECTIVE: To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR.
Prevalence
Macular Edema
Deep Learning
Reproducibility of Results
Thailand
Humans
Incidence
Longitudinal Studies
Aged
Severity of Illness Index
Diabetic Retinopathy
Cell Proliferation
Predictive Value of Tests
Female
Mass Screening
Fundus Oculi
Image Interpretation, Computer-Assisted
Male
Middle Aged
Photography
National Health Programs
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