AIMC Topic: Fundus Oculi

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DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images.

Medical image analysis Apr 30, 2020
Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and follow up of patient, but the screening process can be tiresome and prone to errors. Deep learning approaches have shown promising performance as computer-aided di...
Diagnosis, Computer-Assisted Deep Learning Diabetes Mellitus Fundus Oculi Uncertainty Humans Diabetic Retinopathy
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Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.

Translational vision science & technology Apr 28, 2020
PURPOSE: To compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models.
Artificial Intelligence Algorithms Deep Learning Fundus Oculi Humans Glaucoma
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Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD.

Translational vision science & technology Apr 24, 2020
PURPOSE: To build and validate artificial intelligence (AI)-based models for AMD screening and for predicting late dry and wet AMD progression within 1 and 2 years.
Humans Artificial Intelligence Machine Learning Wet Macular Degeneration Severity of Illness Index Fundus Oculi
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Microaneurysms detection in color fundus images using machine learning based on directional local contrast.

Biomedical engineering online Apr 15, 2020
BACKGROUND: As one of the major complications of diabetes, diabetic retinopathy (DR) is a leading cause of visual impairment and blindness due to delayed diagnosis and intervention. Microaneurysms appear as the earliest symptom of DR. Accurate and re...
Humans Microaneurysm Image Processing, Computer-Assisted Area Under Curve Fundus Oculi Machine Learning Molecular Imaging Time Factors ROC Curve
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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.
Deep Learning ROC Curve Diagnosis, Differential Predictive Value of Tests Humans Algorithms Datasets as Topic Neural Networks, Computer Retrospective Studies Ophthalmoscopy Sensitivity and Specificity Photography Area Under Curve Papilledema Fundus Oculi Retina
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Determinants of Cone and Rod Functions in Geographic Atrophy: AI-Based Structure-Function Correlation.

American journal of ophthalmology Apr 11, 2020
PURPOSE: To investigate the association between retinal microstructure and cone and rod function in geographic atrophy (GA) secondary to age-related macular degeneration (AMD) by using artificial intelligence (AI) algorithms.
Humans Artificial Intelligence Algorithms Male Disease Progression Visual Acuity Tomography, Optical Coherence Prospective Studies Female Aged Retinal Rod Photoreceptor Cells Geographic Atrophy Fundus Oculi Fluorescein Angiography Retinal Cone Photoreceptor Cells
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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 Cloud Computing Humans Diabetes Mellitus Diabetic Retinopathy Mass Screening Neural Networks, Computer New Zealand
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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...
Ophthalmoscopy Fundus Oculi Retina Carotid Arteries Middle Aged Risk Assessment Deep Learning Humans Predictive Value of Tests Prognosis Atherosclerosis Female Ultrasonography Risk Factors Survival Rate Follow-Up Studies Retrospective Studies Male Republic of Korea Cardiovascular Diseases Aged Aged, 80 and over Adult
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Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks.

Journal of medical systems Mar 20, 2020
Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diagnosing of optic nerve head abnormalities such as glaucoma. Many recent works formulated the OD and OC segmentation as a pixel classification task. How...
Natural Language Processing Fundus Oculi Optic Disk Glaucoma Image Processing, Computer-Assisted Humans Neural Networks, Computer
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Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures.

Scientific reports Mar 19, 2020
Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and they are still evolving. However, there are few studies comparing these architectures in the field of...
Diagnostic Techniques, Ophthalmological Humans Glaucoma Algorithms Retrospective Studies Middle Aged Female Optic Nerve Deep Learning Aged Fundus Oculi Male
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