OBJECTIVE: To systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).
Retinal images provide a non-invasive and accessible means to directly visualize human blood vessels and nerve fibers. Growing studies have investigated the intricate microvascular and neural circuitry within the retina, its interactions with other s...
UNLABELLED: is a comparative analysis of algorithms for segmentation of three-dimensional OCT images of human skin using neural networks based on U-Net architecture when training the model on two-dimensional and three-dimensional data.
PURPOSE: The purpose of this study was to develop models that predict which patients with glaucoma will progress to require surgery, combining structured data from electronic health records (EHRs) and retinal fiber layer optical coherence tomography ...
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...
Journal of refractive surgery (Thorofare, N.J. : 1995)
40067669
PURPOSE: To report a deep learning neural network on anterior segment optical coherence tomography (AS-OCT) for automated detection of different keratorefractive laser surgeries-including laser in situ keratomileusis with femtosecond microkeratome (f...
PURPOSE: The purpose of this study was to define structure-function correlation of geographic atrophy (GA) on optical coherence tomography (OCT) and functional testing on microperimetry (MP) based on deep-learning (DL)-quantified spectral-domain OCT ...
BACKGROUND: We aimed to develop and validate an Artificial Intelligence (AI) model that leverages CCTA and optical coherence tomography (OCT) images for automated analysis of plaque characteristics and coronary function.
PURPOSE: Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable scre...
This article proposes an effective and lightweight contextual convolutional neural network architecture called LOCT-Net for classifying retinal diseases. The LOCT-Net adopts nested residual blocks to capture the local patterns from the optical cohere...