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...
Adolescent Visual Acuity Tomography, Optical Coherence Fundus Oculi Male Middle Aged Convolutional Neural Networks Visual Fields Fluorescein Angiography Macula Lutea Retinitis Pigmentosa Humans Neural Networks, Computer Retrospective Studies Female Aged Young Adult Adult
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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...
Optic Neuritis Tomography, Optical Coherence Retinal Ganglion Cells Visual Fields Macula Lutea Adult Male Middle Aged Humans Machine Learning Female
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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 (...
Image Processing, Computer-Assisted Tomography, Optical Coherence Supervised Machine Learning Image Interpretation, Computer-Assisted Humans Macula Lutea Algorithms
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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 Optic Disk Retinal Ganglion Cells Female Aged Intraocular Pressure Male Humans Retrospective Studies Retinal Vessels Visual Fields Glaucoma, Open-Angle Visual Field Tests Fluorescein Angiography Macula Lutea
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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 Visual Acuity Cross-Sectional Studies Tomography, Optical Coherence Fluorescein Angiography Male Fundus Oculi Female Macula Lutea Middle Aged Macular Edema Aged Retinal Neovascularization Deep Learning Aged, 80 and over Retinal Vein Occlusion
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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).
Aged Male Middle Aged Follow-Up Studies Humans Artificial Intelligence Prospective Studies Female Fluorescein Angiography Macula Lutea Retinal Vein Occlusion Visual Acuity Blood-Retinal Barrier Tomography, Optical Coherence Fundus Oculi Macular Edema
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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 ...
Humans Retinopathy of Prematurity Fundus Oculi Image Interpretation, Computer-Assisted Algorithms Optic Disk Infant, Newborn Macula Lutea Infant Deep Learning
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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...
Visual Acuity Fundus Oculi Humans Macula Lutea Neural Networks, Computer Retrospective Studies
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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 ...
Humans Diabetes Mellitus Mass Screening Diabetic Retinopathy Retina Deep Learning Macula Lutea
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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.
Macula Lutea Humans Child, Preschool Cross-Sectional Studies Child Deep Learning Tomography, Optical Coherence Fundus Oculi
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