AIMC Topic: Macula Lutea
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Classification of fundus autofluorescence images based on macular function in retinitis pigmentosa using convolutional neural networks.
Japanese journal of ophthalmology
Feb 12, 2025
PURPOSE: To determine whether convolutional neural networks (CNN) can classify the severity of central vision loss using fundus autofluorescence (FAF) images and color fundus images of retinitis pigmentosa (RP), and to evaluate the utility of those i...
Macular patterns of neuronal and visual field loss in recovered optic neuritis identified by machine learning.
Scientific reports
Dec 28, 2024
We used machine learning to investigate the residual visual field (VF) deficits and macula retinal ganglion cell (RGC) thickness loss patterns in recovered optic neuritis (ON). We applied archetypal analysis (AA) to 377 same-day pairings of 10-2 VF a...
CircWaveDL: Modeling of optical coherence tomography images based on a new supervised tensor-based dictionary learning for classification of macular abnormalities.
Artificial intelligence in medicine
Dec 24, 2024
Modeling Optical Coherence Tomography (OCT) images is crucial for numerous image processing applications and aids ophthalmologists in the early detection of macular abnormalities. Sparse representation-based models, particularly dictionary learning (...
Detection of glaucoma progression on longitudinal series of en-face macular optical coherence tomography angiography images with a deep learning model.
The British journal of ophthalmology
Nov 22, 2024
BACKGROUND/AIMS: To design a deep learning (DL) model for the detection of glaucoma progression with a longitudinal series of macular optical coherence tomography angiography (OCTA) images.
Middle Aged
Disease Progression
Deep Learning
ROC Curve
Follow-Up Studies
Tomography, Optical Coherence
Retinal Vessels
Optic Disk
Female
Glaucoma, Open-Angle
Retinal Ganglion Cells
Visual Fields
Aged
Visual Field Tests
Humans
Intraocular Pressure
Fluorescein Angiography
Retrospective Studies
Male
Macula Lutea
Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model.
Ophthalmology. Retina
Oct 24, 2024
PURPOSE: To test the diagnostic performance of an artificial intelligence algorithm for detecting and segmenting macular neovascularization (MNV) with OCT and OCT angiography (OCTA) in eyes with macular edema from various diagnoses.
Humans
Diabetic Retinopathy
Algorithms
Prospective Studies
Cross-Sectional Studies
Male
Middle Aged
Deep Learning
Female
Aged
Visual Acuity
Aged, 80 and over
Tomography, Optical Coherence
Fundus Oculi
Macular Edema
Fluorescein Angiography
Macula Lutea
Retinal Neovascularization
Retinal Vein Occlusion
Artificial intelligence-based extraction of quantitative ultra-widefield fluorescein angiography parameters in retinal vein occlusion.
Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
Aug 31, 2024
OBJECTIVE: To examine the association between quantitative vascular parameters extracted from intravenous fluorescein angiography (IVFA) and baseline clinical characteristics of patients with retinal vein occlusion (RVO).
Morphological Rule-Constrained Object Detection of Key Structures in Infant Fundus Image.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 9, 2024
The detection of optic disc and macula is an essential step for ROP (Retinopathy of prematurity) zone segmentation and disease diagnosis. This paper aims to enhance deep learning-based object detection with domain-specific morphological rules. Based ...
Estimation of best corrected visual acuity based on deep neural network.
Scientific reports
Oct 24, 2022
In this study, we investigated a convolutional neural network (CNN)-based framework for the estimation of the best-corrected visual acuity (BCVA) from fundus images. First, we collected 53,318 fundus photographs from the Gyeongsang National Universit...
Automated image curation in diabetic retinopathy screening using deep learning.
Scientific reports
Jul 1, 2022
Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require curation, a laborious task to perform manually. We developed and validated single and multi-output ...
Identification of Sex and Age from Macular Optical Coherence Tomography and Feature Analysis Using Deep Learning.
American journal of ophthalmology
Sep 25, 2021
PURPOSE: To develop deep learning models for identification of sex and age from macular optical coherence tomography (OCT) and to analyze the features for differentiation of sex and age.
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