AIMC Topic: Retina

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CycleGAN-based deep learning technique for artifact reduction in fundus photography.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie May 2, 2020
PURPOSE: A low quality of fundus photograph with artifacts may lead to false diagnosis. Recently, a cycle-consistent generative adversarial network (CycleGAN) has been introduced as a tool to generate images without matching paired images. Therefore,...
Humans Algorithms Retina Artifacts Diagnostic Techniques, Ophthalmological Photography Deep Learning Image Processing, Computer-Assisted Fundus Oculi
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Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.

The Lancet. Digital health Apr 23, 2020
BACKGROUND: Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings ...
Health Care Costs Photography Physical Examination Retina Telemedicine Diabetic Retinopathy Cost-Benefit Analysis Diagnostic Techniques, Ophthalmological Ophthalmology Singapore Artificial Intelligence Models, Biological Machine Learning Image Processing, Computer-Assisted Mass Screening Adult Aged Decision Trees Diabetes Mellitus Humans Sensitivity and Specificity Middle Aged
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Learned optical flow for intra-operative tracking of the retinal fundus.

International journal of computer assisted radiology and surgery Apr 22, 2020
PURPOSE: Sustained delivery of regenerative retinal therapies by robotic systems requires intra-operative tracking of the retinal fundus. We propose a supervised deep convolutional neural network to densely predict semantic segmentation and optical f...
Neural Networks, Computer Deep Learning Humans Algorithms Retina
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Machine learning applied to retinal image processing for glaucoma detection: review and perspective.

Biomedical engineering online Apr 15, 2020
INTRODUCTION: This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Fu...
Humans Glaucoma Machine Learning Image Processing, Computer-Assisted Retina
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DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images.

Translational vision science & technology Apr 15, 2020
PURPOSE: To remove blood vessel shadows from optical coherence tomography (OCT) images of the optic nerve head (ONH).
Deep Learning Tomography, Optical Coherence Humans Algorithms Retina Optic Disk
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Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

The New England journal of medicine Apr 14, 2020
BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied.
Sensitivity and Specificity Deep Learning ROC Curve Photography Area Under Curve Papilledema Fundus Oculi Diagnosis, Differential Predictive Value of Tests Retina Humans Datasets as Topic Neural Networks, Computer Algorithms Ophthalmoscopy Retrospective Studies
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Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke.

PloS one Apr 10, 2020
Convolutional Neural Networks (CNNs) have become a prominent method of AI implementation in medical classification tasks. Grading Diabetic Retinopathy (DR) has been at the forefront of the development of AI for ophthalmology. However, major obstacles...
Fundus Oculi Retina Diabetes Mellitus Cloud Computing Humans Mass Screening New Zealand Diabetic Retinopathy Neural Networks, Computer
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Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images.

American journal of ophthalmology Mar 25, 2020
PURPOSE: The prediction of atherosclerosis using retinal fundus images and deep learning has not been shown possible. The purpose of this study was to develop a deep learning model which predicted atherosclerosis by using retinal fundus images and to...
Deep Learning Ultrasonography Follow-Up Studies Predictive Value of Tests Cardiovascular Diseases Middle Aged Risk Assessment Carotid Arteries Ophthalmoscopy Atherosclerosis Fundus Oculi Retina Female Risk Factors Aged Survival Rate Male Aged, 80 and over Adult Prognosis Retrospective Studies Republic of Korea Humans
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Deep Learning for Automated Sorting of Retinal Photographs.

Ophthalmology. Retina Mar 17, 2020
PURPOSE: Though the domain of big data and artificial intelligence in health care continues to evolve, there is a lack of systemic methods to improve data quality and streamline the preparation process. To address this, we aimed to develop an automat...
Image Interpretation, Computer-Assisted Retinal Diseases Humans Deep Learning Retrospective Studies Artificial Intelligence Retina Cross-Sectional Studies Algorithms
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Application of a Deep Machine Learning Model for Automatic Measurement of EZ Width in SD-OCT Images of RP.

Translational vision science & technology Mar 17, 2020
PURPOSE: We applied a deep convolutional neural network model for automatic identification of ellipsoid zone (EZ) in spectral domain optical coherence tomography B-scans of retinitis pigmentosa (RP).
Retinal Pigment Epithelium Tomography, Optical Coherence Humans Retinitis Pigmentosa Retina Machine Learning
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