AIMC Topic: Macula Lutea
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GENERATIVE DEEP LEARNING APPROACH TO PREDICT POSTTREATMENT OPTICAL COHERENCE TOMOGRAPHY IMAGES OF AGE-RELATED MACULAR DEGENERATION AFTER 12 MONTHS.
Retina (Philadelphia, Pa.)
Jun 1, 2025
PURPOSE: Predicting long-term anatomical responses in neovascular age-related macular degeneration patients is critical for patient-specific management. This study validates a generative deep learning model to predict 12-month posttreatment optical c...
Time Factors
Angiogenesis Inhibitors
Follow-Up Studies
Visual Acuity
Aged
Aged, 80 and over
Male
Deep Learning
Fluorescein Angiography
Macula Lutea
Ranibizumab
Wet Macular Degeneration
Tomography, Optical Coherence
Fundus Oculi
Vascular Endothelial Growth Factor A
Intravitreal Injections
Humans
Retrospective Studies
Female
Application of generative adversarial networks in the restoration of blurred optical coherence tomography images caused by optical media opacity in eyes.
BMJ open ophthalmology
May 26, 2025
PURPOSE: To assess the application of generative adversarial networks (GANs) to restore the blurred optical coherence tomography (OCT) images caused by optical media opacity in eyes.
Anomaly Detection in Retinal OCT Images With Deep Learning-Based Knowledge Distillation.
Translational vision science & technology
Mar 3, 2025
PURPOSE: The purpose of this study was to develop a robust and general purpose artificial intelligence (AI) system that allows the identification of retinal optical coherence tomography (OCT) volumes with pathomorphological manifestations not present...
The Associations Between Myopia and Fundus Tessellation in School Children: A Comparative Analysis of Macular and Peripapillary Regions Using Deep Learning.
Translational vision science & technology
Jan 2, 2025
PURPOSE: To evaluate the refractive differences among school-aged children with macular or peripapillary fundus tessellation (FT) distribution patterns, using fundus tessellation density (FTD) quantified by deep learning (DL) technology.
Prediction of Axial Length From Macular Optical Coherence Tomography Using Deep Learning Model.
Translational vision science & technology
Sep 3, 2024
PURPOSE: The purpose of this study was to develop a deep learning model for predicting the axial length (AL) of eyes using optical coherence tomography (OCT) images.
A Novel Artificial Intelligence-Based Classification of Highly Myopic Eyes Based on Visual Function and Fundus Features.
Translational vision science & technology
Sep 3, 2024
PURPOSE: To develop a novel classification of highly myopic eyes using artificial intelligence (AI) and investigate its relationship with contrast sensitivity function (CSF) and fundus features.
Comparative Analysis of Macular and Optic Disc Perfusion Pre and Post Silicone Oil Removal: A Machine Learning Approach.
Studies in health technology and informatics
Aug 22, 2024
In the realm of ophthalmic surgeries, silicone oil is often utilized as a tamponade agent for repairing retinal detachments, but it necessitates subsequent removal. This study harnesses the power of machine learning to analyze the macular and optic d...
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...
Visual Field Prognosis From Macula and Circumpapillary Spectral Domain Optical Coherence Tomography.
Translational vision science & technology
Jun 3, 2024
PURPOSE: To explore the structural-functional loss relationship from optic-nerve-head- and macula-centred spectral-domain (SD) Optical Coherence Tomography (OCT) images in the full spectrum of glaucoma patients using deep-learning methods.
Deep Learning for Diagnosing and Segmenting Choroidal Neovascularization in OCT Angiography in a Large Real-World Data Set.
Translational vision science & technology
Apr 3, 2023
PURPOSE: To diagnose and segment choroidal neovascularization (CNV) in a real-world multicenter clinical OCT angiography (OCTA) data set using deep learning.
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