AIMC Topic: Wet Macular Degeneration

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Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD.

Translational vision science & technology Apr 24, 2020
PURPOSE: To build and validate artificial intelligence (AI)-based models for AMD screening and for predicting late dry and wet AMD progression within 1 and 2 years.
Fundus Oculi Wet Macular Degeneration Humans Artificial Intelligence Machine Learning Severity of Illness Index
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Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs.

Clinical & experimental ophthalmology Jul 25, 2019
IMPORTANCE: Detection of early onset neovascular age-related macular degeneration (AMD) is critical to protecting vision.
Algorithms Retrospective Studies Female Reproducibility of Results Sensitivity and Specificity Humans False Positive Reactions ROC Curve Fluorescein Angiography Visual Acuity Wet Macular Degeneration Male Predictive Value of Tests Choroidal Neovascularization Middle Aged Photography Area Under Curve Ophthalmologists Fundus Oculi Diagnosis, Computer-Assisted Deep Learning
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Feasibility of support vector machine learning in age-related macular degeneration using small sample yielding sparse optical coherence tomography data.

Acta ophthalmologica Mar 6, 2019
PURPOSE: A retrospective pilot study is conducted to demonstrate the utility of a novel support vector machine learning (SVML) algorithm in a small three-dimensional (3D) sample yielding sparse optical coherence tomography (spOCT) data for the automa...
Time Factors ROC Curve Imaging, Three-Dimensional Support Vector Machine Follow-Up Studies Male Middle Aged Disease Progression Pilot Projects Tomography, Optical Coherence Feasibility Studies Macula Lutea Wet Macular Degeneration Humans Algorithms Prognosis Retrospective Studies Female Aged Aged, 80 and over
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Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration.

International ophthalmology May 9, 2018
PURPOSE: To predict exudative age-related macular degeneration (AMD), we combined a deep convolutional neural network (DCNN), a machine-learning algorithm, with Optos, an ultra-wide-field fundus imaging system.
Ophthalmoscopy Wet Macular Degeneration Sensitivity and Specificity Male Middle Aged Diagnosis, Computer-Assisted Deep Learning Algorithms Female Aged Aged, 80 and over Humans Neural Networks, Computer
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Predicting Visual Acuity by Using Machine Learning in Patients Treated for Neovascular Age-Related Macular Degeneration.

Ophthalmology Feb 14, 2018
PURPOSE: To predict, by using machine learning, visual acuity (VA) at 3 and 12 months in patients with neovascular age-related macular degeneration (AMD) after initial upload of 3 anti-vascular endothelial growth factor (VEGF) injections.
Humans Disease Progression Angiogenesis Inhibitors Follow-Up Studies Visual Acuity Intravitreal Injections Reproducibility of Results Machine Learning Vascular Endothelial Growth Factor A Wet Macular Degeneration Aged Algorithms Aged, 80 and over Choroidal Neovascularization Databases, Factual Male Female Middle Aged
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Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning.

Ophthalmology Dec 8, 2017
PURPOSE: Development and validation of a fully automated method to detect and quantify macular fluid in conventional OCT images.
Diagnosis, Computer-Assisted Deep Learning ROC Curve Visual Acuity Tomography, Optical Coherence Macular Edema Wet Macular Degeneration Subretinal Fluid Retinal Vein Occlusion Humans Diabetic Retinopathy Female Reproducibility of Results Aged Male Middle Aged
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Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Nov 20, 2017
PURPOSE: Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT).
Macula Lutea Humans Wet Macular Degeneration Neural Networks, Computer Machine Learning Reproducibility of Results Tomography, Optical Coherence
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Use of a Neural Net to Model the Impact of Optical Coherence Tomography Abnormalities on Vision in Age-related Macular Degeneration.

American journal of ophthalmology Oct 31, 2017
PURPOSE: To develop a neural network for the estimation of visual acuity from optical coherence tomography (OCT) images of patients with neovascular age-related macular degeneration (AMD) and to demonstrate its use to model the impact of specific con...
Reproducibility of Results Aged Male Humans Tomography, Optical Coherence Middle Aged Neural Networks, Computer Retrospective Studies Visual Acuity Fundus Oculi Female Fluorescein Angiography Macula Lutea Wet Macular Degeneration
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Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration.

Ophthalmology. Retina May 31, 2017
PURPOSE: To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascul...
Tomography, Optical Coherence Fluorescein Angiography Humans Fundus Oculi Reproducibility of Results Macula Lutea Machine Learning Vascular Endothelial Growth Factor A Angiogenesis Inhibitors Prospective Studies Ranibizumab Treatment Outcome Prognosis Intravitreal Injections Retinal Pigment Epithelium Follow-Up Studies Wet Macular Degeneration Visual Acuity
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Fluid fluctuations assessed with artificial intelligence during the maintenance phase impact anti-vascular endothelial growth factor visual outcomes in a multicentre, routine clinical care national age-related macular degeneration database.

The British journal of ophthalmology Sep 23, 2025
AIM: To evaluate the impact of fluid volume fluctuations quantified with artificial intelligence in optical coherence tomography scans during the maintenance phase and visual outcomes at 12 and 24 months in a real-world, multicentre, national cohort ...
Databases, Factual Female Aged Aged, 80 and over Male Humans Intravitreal Injections Artificial Intelligence Angiogenesis Inhibitors Fluorescein Angiography Ranibizumab Follow-Up Studies Wet Macular Degeneration Visual Acuity Subretinal Fluid Tomography, Optical Coherence Vascular Endothelial Growth Factor A
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