AIMC Topic: Macular Edema
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Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.
Eye (London, England)
Apr 21, 2017
PurposeThe purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets.MethodsWe developed an image analysis pipeline tailored towards OCT images that consists of five ste...
Multimodal Imaging in Diabetic Macular Edema.
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Jan 29, 2017
Throughout ophthalmic history it has been shown that progress has gone hand in hand with technological breakthroughs. In the past, fluorescein angiography and fundus photographs were the most commonly used imaging modalities in the management of diab...
Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images.
BMJ open
May 31, 2025
OBJECTIVES: To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.
Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response.
Translational vision science & technology
Dec 2, 2024
PURPOSE: Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macular edema (DME), and predict anti-vascular endothelial growth factor (anti-VEGF) therapy response.
Deep learning-based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis.
Indian journal of ophthalmology
May 1, 2023
Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The...
Advancing Diabetic Retinopathy Diagnosis: Leveraging Optical Coherence Tomography Imaging with Convolutional Neural Networks.
Romanian journal of ophthalmology
Jan 1, 2023
Diabetic retinopathy (DR) is a vision-threatening complication of diabetes, necessitating early and accurate diagnosis. The combination of optical coherence tomography (OCT) imaging with convolutional neural networks (CNNs) has emerged as a promising...
Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans.
Translational vision science & technology
Sep 1, 2022
PURPOSE: To develop an automated method based on deep learning (DL) to classify macular edema (ME) from the evaluation of optical coherence tomography (OCT) scans.
SYSTEMATIC CORRELATION OF CENTRAL SUBFIELD THICKNESS WITH RETINAL FLUID VOLUMES QUANTIFIED BY DEEP LEARNING IN THE MAJOR EXUDATIVE MACULAR DISEASES.
Retina (Philadelphia, Pa.)
May 1, 2022
PURPOSE: To investigate the correlation of volumetric measurements of intraretinal (IRF) and subretinal fluid obtained by deep learning and central retinal subfield thickness (CSFT) based on optical coherence tomography in retinal vein occlusion, dia...
DEVELOPMENT AND VALIDATION OF AN EXPLAINABLE ARTIFICIAL INTELLIGENCE FRAMEWORK FOR MACULAR DISEASE DIAGNOSIS BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.
Retina (Philadelphia, Pa.)
Mar 1, 2022
PURPOSE: To develop and validate an artificial intelligence framework for identifying multiple retinal lesions at image level and performing an explainable macular disease diagnosis at eye level in optical coherence tomography images.
Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning.
JAMA ophthalmology
Feb 1, 2022
IMPORTANCE: Anomaly detectors could be pursued for retinal diagnoses based on artificial intelligence systems that may not have access to training examples for all retinal diseases in all phenotypic presentations. Possible applications could include ...
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