AIMC Topic: Tomography, Optical Coherence
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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).
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head.
Scientific reports
Oct 8, 2019
Optical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and management of various ocular and neuro-ocular pathologies. However, the ...
Noise Adaptation Generative Adversarial Network for Medical Image Analysis.
IEEE transactions on medical imaging
Sep 30, 2019
Machine learning has been widely used in medical image analysis under an assumption that the training and test data are under the same feature distributions. However, medical images from difference devices or the same device with different parameter ...
Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study.
BMJ open
Sep 27, 2019
OBJECTIVE: To evaluate the diagnostic accuracy of keratoconus using deep learning of the colour-coded maps measured with the swept-source anterior segment optical coherence tomography (AS-OCT).
Automated Detection of Vulnerable Plaque for Intravascular Optical Coherence Tomography Images.
Cardiovascular engineering and technology
Sep 18, 2019
PURPOSE: Vulnerable plaque detection is important to acute coronary syndrome (ACS) diagnosis. In recent years, intravascular optical coherence tomography (IVOCT) imaging has been used for vulnerable plaque detection. Current automated detection metho...
Deep learning based retinal OCT segmentation.
Computers in biology and medicine
Sep 17, 2019
We look at the recent application of deep learning (DL) methods in automated fine-grained segmentation of spectral domain optical coherence tomography (OCT) images of the retina. We describe a new method combining fully convolutional networks (FCN) w...
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study.
The Lancet. Digital health
Sep 5, 2019
BACKGROUND: Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifie...
DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images.
Physics in medicine and biology
Sep 4, 2019
Speckle is a major quality degrading factor in optical coherence tomography (OCT) images. In this work we propose a new deep learning network for speckle reduction in retinal OCT images, termed DeSpecNet. Unlike traditional algorithms, the model can ...
Machine Learning Models for Diagnosing Glaucoma from Retinal Nerve Fiber Layer Thickness Maps.
Ophthalmology. Glaucoma
Aug 23, 2019
PURPOSE: To assess the diagnostic accuracy of multiple machine learning models using full retinal nerve fiber layer (RNFL) thickness maps in detecting glaucoma.
Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images.
American journal of ophthalmology
Aug 22, 2019
PURPOSE: To develop and test deep learning classifiers that detect gonioscopic angle closure and primary angle closure disease (PACD) based on fully automated analysis of anterior segment OCT (AS-OCT) images.
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