AIMC Topic: Choroid
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Classification of pachychoroid on optical coherence tomography using deep learning.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Feb 22, 2021
PURPOSE: Pachychoroid is characterized by dilated Haller vessels and choriocapillaris attenuation that are seen on optical coherence tomography (OCT) B-scans. This study investigated the feasibility of using deep learning (DL) models to classify pach...
Choroid Segmentation of Retinal OCT Images Based on CNN Classifier and - Fitter.
Computational and mathematical methods in medicine
Jan 15, 2021
Optical coherence tomography (OCT) is a noninvasive cross-sectional imaging technology used to examine the retinal structure and pathology of the eye. Evaluating the thickness of the choroid using OCT images is of great interests for clinicians and r...
Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning.
IEEE journal of biomedical and health informatics
Dec 4, 2020
The choroid provides oxygen and nourishment to the outer retina thus is related to the pathology of various ocular diseases. Optical coherence tomography (OCT) is advantageous in visualizing and quantifying the choroid in vivo. However, its applicati...
Deep phenotype unsupervised machine learning revealed the significance of pachychoroid features in etiology and visual prognosis of age-related macular degeneration.
Scientific reports
Oct 28, 2020
Unsupervised machine learning has received increased attention in clinical research because it allows researchers to identify novel and objective viewpoints for diseases with complex clinical characteristics. In this study, we applied a deep phenotyp...
Automated Recognition of Retinal Pigment Epithelium Cells on Limited Training Samples Using Neural Networks.
Translational vision science & technology
Jun 16, 2020
PURPOSE: To develop a neural network (NN)-based approach, with limited training resources, that identifies and counts the number of retinal pigment epithelium (RPE) cells in confocal microscopy images obtained from cell culture or mice RPE/choroid fl...
Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks.
The British journal of ophthalmology
Jun 4, 2020
AIMS: To investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tom...
Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography.
PloS one
Apr 16, 2020
This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular...
Utility of a public-available artificial intelligence in diagnosis of polypoidal choroidal vasculopathy.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Nov 4, 2019
PURPOSE: To investigate the feasibility of training an artificial intelligence (AI) on a public-available AI platform to diagnose polypoidal choroidal vasculopathy (PCV) using indocyanine green angiography (ICGA).
Running pattern of choroidal vessel in en face OCT images determined by machine learning-based quantitative method.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Jun 24, 2019
PURPOSE: To evaluate the new method to quantitate the running pattern of the vessels in Haller's layer in en face optical coherence tomographic (OCT) images using the new algorithm.
Automated segmentation of en face choroidal images obtained by optical coherent tomography by machine learning.
Japanese journal of ophthalmology
Oct 6, 2018
PURPOSE: To develop an automated method to segment the choroidal layers of en face optical coherent tomography (OCT) images by machine learning.
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