AIMC Topic: Ophthalmoscopy

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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.
Retrospective Studies Sensitivity and Specificity Deep Learning ROC Curve Humans Neural Networks, Computer Algorithms Retina Datasets as Topic Ophthalmoscopy Photography Papilledema Diagnosis, Differential Predictive Value of Tests Area Under Curve Fundus Oculi
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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...
Prognosis Retrospective Studies Republic of Korea Female Risk Factors Humans Ophthalmoscopy Aged Atherosclerosis Aged, 80 and over Fundus Oculi Male Adult Retina Deep Learning Cardiovascular Diseases Survival Rate Carotid Arteries Middle Aged Ultrasonography Risk Assessment Follow-Up Studies Predictive Value of Tests
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Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach.

Translational vision science & technology Feb 14, 2020
PURPOSE: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blo...
Neural Networks, Computer Infant, Newborn Child Area Under Curve Humans Ophthalmoscopy Retinopathy of Prematurity Retinal Vessels
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Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy.

International ophthalmology Feb 23, 2019
PURPOSE: We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, to detect treatment-naïve proliferative diabetic retinopathy (PDR).
Deep Learning Area Under Curve Ophthalmoscopy Diabetic Retinopathy Female Aged Adult Sensitivity and Specificity Male Middle Aged Diagnosis, Computer-Assisted Humans
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Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

Ophthalmology. Retina Jan 31, 2019
PURPOSE: Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of...
Image Processing, Computer-Assisted Ophthalmoscopy Retinopathy of Prematurity Female Humans Infant, Newborn Male Neural Networks, Computer ROC Curve Algorithms
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Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.

Scientific reports Oct 2, 2018
The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology. A training dataset consisted of 1,364 color fundu...
ROC Curve Fundus Oculi Image Interpretation, Computer-Assisted Datasets as Topic Adult Ophthalmoscopy Male Humans Photography Middle Aged Glaucoma Deep Learning Female Aged
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Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration.

International ophthalmology May 9, 2018
PURPOSE: To predict exudative age-related macular degeneration (AMD), we combined a deep convolutional neural network (DCNN), a machine-learning algorithm, with Optos, an ultra-wide-field fundus imaging system.
Aged, 80 and over Sensitivity and Specificity Male Middle Aged Diagnosis, Computer-Assisted Deep Learning Ophthalmoscopy Wet Macular Degeneration Neural Networks, Computer Algorithms Female Aged Humans
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Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.

Scientific reports Aug 25, 2017
Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep le...
Fundus Oculi Ophthalmoscopy Retinal Detachment Sensitivity and Specificity Male Middle Aged Deep Learning ROC Curve Humans Machine Learning Female Aged
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Multimodal Imaging in Diabetic Macular Edema.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) Jan 29, 2017
Throughout ophthalmic history it has been shown that progress has gone hand in hand with technological breakthroughs. In the past, fluorescein angiography and fundus photographs were the most commonly used imaging modalities in the management of diab...
Ophthalmoscopy Macular Edema Optics and Photonics Humans Optical Imaging Diabetic Retinopathy Diagnostic Techniques, Ophthalmological Multimodal Imaging Fluorescein Angiography Tomography, Optical Coherence
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A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images.

IEEE transactions on bio-medical engineering Feb 26, 2016
GOAL: In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model.
Ophthalmoscopy Photography Humans Models, Cardiovascular Machine Learning Retinal Artery Image Interpretation, Computer-Assisted Computer Simulation Pattern Recognition, Automated Reproducibility of Results Retinal Diseases Sensitivity and Specificity Fluorescein Angiography Models, Statistical
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