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Macular Degeneration

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A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography.

JAMA ophthalmology
IMPORTANCE: The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully autom...

Deep Learning-Based System for Disease Screening and Pathologic Region Detection From Optical Coherence Tomography Images.

Translational vision science & technology
PURPOSE: This study was designed to apply deep learning models in retinal disease screening and lesion detection based on optical coherence tomography (OCT) images.

Advancing Diabetic Retinopathy Diagnosis: Leveraging Optical Coherence Tomography Imaging with Convolutional Neural Networks.

Romanian journal of ophthalmology
Diabetic retinopathy (DR) is a vision-threatening complication of diabetes, necessitating early and accurate diagnosis. The combination of optical coherence tomography (OCT) imaging with convolutional neural networks (CNNs) has emerged as a promising...

A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans.

Deep Learning-Based Modeling of the Dark Adaptation Curve for Robust Parameter Estimation.

Translational vision science & technology
PURPOSE: This study investigates deep-learning (DL) sequence modeling techniques to reliably fit dark adaptation (DA) curves and estimate their key parameters in patients with age-related macular degeneration (AMD) to improve robustness and curve pre...

A SYSTEMATIC REVIEW OF DEEP LEARNING APPLICATIONS FOR OPTICAL COHERENCE TOMOGRAPHY IN AGE-RELATED MACULAR DEGENERATION.

Retina (Philadelphia, Pa.)
PURPOSE: To survey the current literature regarding applications of deep learning to optical coherence tomography in age-related macular degeneration (AMD).

Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning.

Investigative ophthalmology & visual science
PURPOSE: Luminance contrast is the fundamental building block of human spatial vision. Therefore contrast sensitivity, the reciprocal of contrast threshold required for target detection, has been a barometer of human visual function. Although retinal...

Automatic Screening and Identifying Myopic Maculopathy on Optical Coherence Tomography Images Using Deep Learning.

Translational vision science & technology
PURPOSE: The purpose of this study was to engineer deep learning (DL) models that can identify myopic maculopathy in patients with high myopia based on optical coherence tomography (OCT) images.

Artificial intelligence-based predictions in neovascular age-related macular degeneration.

Current opinion in ophthalmology
PURPOSE OF REVIEW: Predicting treatment response and optimizing treatment regimen in patients with neovascular age-related macular degeneration (nAMD) remains challenging. Artificial intelligence-based tools have the potential to increase confidence ...

Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and typically require advanced imaging modalities such as fundus autoflu...