AIMC Topic: Intravitreal Injections
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Correlation of limited-early-response status with 12-month CST, BVA, and machine learning-quantified retinal fluid in diabetic macular oedema in routine clinical practice.
Eye (London, England)
Jun 18, 2024
BACKGROUND/OBJECTIVES: Anti-VEGF treatment response in DMO has been measured by changes in the central subfield thickness (CST) and best visual acuity (BVA) outcomes at 3 months after initial treatment, termed early or limited early response (ER/LER)...
Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration.
Acta ophthalmologica
Dec 22, 2023
PURPOSE: To assess the suitability of machine learning (ML) techniques in predicting the development of fibrosis and atrophy in patients with neovascular age-related macular degeneration (nAMD), receiving anti-VEGF treatment over a 36-month period.
Humans
Machine Learning
Retrospective Studies
Angiogenesis Inhibitors
Follow-Up Studies
Female
Ranibizumab
Visual Acuity
Vascular Endothelial Growth Factor A
Aged
Wet Macular Degeneration
Tomography, Optical Coherence
Fibrosis
Aged, 80 and over
Atrophy
Fundus Oculi
Intravitreal Injections
Male
Fluorescein Angiography
Preliminary analysis of predicting the first recurrence in patients with neovascular age-related macular degeneration using deep learning.
BMC ophthalmology
Dec 7, 2023
BACKGROUND: To predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF).
Predicting Visual Acuity Responses to Anti-VEGF Treatment in the Comparison of Age-related Macular Degeneration Treatments Trials Using Machine Learning.
Ophthalmology. Retina
Nov 24, 2023
PURPOSE: To evaluate multiple machine learning (ML) models for predicting 2-year visual acuity (VA) responses to anti-vascular endothelial growth factor (anti-VEGF) treatment in the Comparison of Age-related Macular Degeneration (AMD) Treatments Tria...
Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.
Eye (London, England)
Sep 5, 2023
PURPOSE: To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD).
Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD.
Eye (London, England)
Jun 13, 2023
PURPOSE: To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated...
Correlation of vascular and fluid-related parameters in neovascular age-related macular degeneration using deep learning.
Acta ophthalmologica
Aug 1, 2022
PURPOSE: To identify correlations between the vascular characteristics of macular neovascularization (MNV) obtained by optical coherence tomography angiography (OCTA) and distinct retinal fluid volumes in neovascular age-related macular degeneration ...
Therapeutic response in the HAWK and HARRIER trials using deep learning in retinal fluid volume and compartment analysis.
Eye (London, England)
May 6, 2022
OBJECTIVES: To assess the therapeutic response to brolucizumab and aflibercept by deep learning/OCT-based analysis of macular fluid volumes in neovascular age-related macular degeneration.
Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study.
PloS one
Feb 14, 2022
PURPOSE: To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source o...
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...
Retinal Vein Occlusion
Male
Middle Aged
Angiogenesis Inhibitors
Follow-Up Studies
Intravitreal Injections
Ranibizumab
Wet Macular Degeneration
Macular Edema
Vascular Endothelial Growth Factor A
Retrospective Studies
Female
Aged
Aged, 80 and over
Humans
Diabetic Retinopathy
Machine Learning
Prognosis
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