AIMC Topic: Retinal Neovascularization
Clear Filters Showing 1 to 10 of 10 articles
Retinal Vascularization Rate Predicts Retinopathy of Prematurity and Remains Unaffected by Low-Dose Bevacizumab Treatment.
American journal of ophthalmology
Mar 5, 2025
PURPOSE: To assess the rate of retinal vascularization derived from ultra-widefield (UWF) imaging-based retinopathy of prematurity (ROP) screening as predictor of type 1 ROP and characterize the effect of anti-vascular endothelial growth factor (anti...
Prediction of fellow eye neovascularization in type 3 macular neovascularization (Retinal angiomatous proliferation) using deep learning.
PloS one
Oct 30, 2024
PURPOSE: To establish a deep learning artificial intelligence model to predict the risk of long-term fellow eye neovascularization in unilateral type 3 macular neovascularization (MNV).
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
Aged, 80 and over
Cross-Sectional Studies
Male
Middle Aged
Deep Learning
Algorithms
Prospective Studies
Female
Aged
Fluorescein Angiography
Macula Lutea
Visual Acuity
Retinal Neovascularization
Tomography, Optical Coherence
Retinal Vein Occlusion
Fundus Oculi
Macular Edema
Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography.
Scientific reports
Apr 29, 2021
This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convoluti...
A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy model.
Scientific reports
Mar 2, 2018
Preclinical studies of vascular retinal diseases rely on the assessment of developmental dystrophies in the oxygen induced retinopathy rodent model. The quantification of vessel tufts and avascular regions is typically computed manually from flat mou...
Fully automated, deep learning segmentation of oxygen-induced retinopathy images.
JCI insight
Dec 21, 2017
Oxygen-induced retinopathy (OIR) is a widely used model to study ischemia-driven neovascularization (NV) in the retina and to serve in proof-of-concept studies in evaluating antiangiogenic drugs for ocular, as well as nonocular, diseases. The primary...
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...
AI Quantification of Vascular Lesions in Mouse Fundus Fluorescein Angiography.
Translational vision science & technology
Jun 2, 2025
PURPOSE: Quantifying vascular leakage in fundus fluorescein angiography (FFA) is a critical endpoint in preclinical models of diseases such as neovascular age-related macular degeneration, retinopathy of prematurity, and diabetic retinopathy. Traditi...
DEEP LEARNING FOR AUTOMATIC PREDICTION OF EARLY ACTIVATION OF TREATMENT-NAIVE NONEXUDATIVE MACULAR NEOVASCULARIZATIONS IN AGE-RELATED MACULAR DEGENERATION.
Retina (Philadelphia, Pa.)
Aug 1, 2024
BACKGROUND: Around 30% of nonexudative macular neovascularizations exudate within 2 years from diagnosis in patients with age-related macular degeneration. The aim of this study is to develop a deep learning classifier based on optical coherence tomo...
Deep Learning Detection of Sea Fan Neovascularization From Ultra-Widefield Color Fundus Photographs of Patients With Sickle Cell Hemoglobinopathy.
JAMA ophthalmology
Feb 1, 2021
IMPORTANCE: Adherence to screening for vision-threatening proliferative sickle cell retinopathy is limited among patients with sickle cell hemoglobinopathy despite guidelines recommending dilated fundus examinations beginning in childhood. An automat...
Anemia, Sickle Cell
Image Interpretation, Computer-Assisted
Observer Variation
Pattern Recognition, Automated
Retinal Neovascularization
Retinal Vessels
Fluorescein Angiography
Photography
Male
Middle Aged
Deep Learning
Predictive Value of Tests
Reproducibility of Results
Young Adult
Adult
Cross-Sectional Studies
Humans
Retrospective Studies
Female
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823