AIMC Topic: Fundus Oculi
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An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning.
Computational and mathematical methods in medicine
May 31, 2021
Diabetic retinopathy occurs as a result of the harmful effects of diabetes on the eyes. Diabetic retinopathy is also a disease that should be diagnosed early. If not treated early, vision loss may occur. It is estimated that one third of more than ha...
A deep learning system for detecting diabetic retinopathy across the disease spectrum.
Nature communications
May 28, 2021
Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR ...
Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning.
Sensors (Basel, Switzerland)
May 26, 2021
Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stoppi...
Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs.
Neurology
May 19, 2021
OBJECTIVE: To evaluate the performance of a deep learning system (DLS) in classifying the severity of papilledema associated with increased intracranial pressure on standard retinal fundus photographs.
Predicting sex from retinal fundus photographs using automated deep learning.
Scientific reports
May 13, 2021
Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reporte...
Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning.
International journal of computer assisted radiology and surgery
May 8, 2021
PURPOSE: The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise segmentation of the optic cup (OC) and optic disc (OD). In recent years, convolution neural network...
Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis.
Eye (London, England)
May 6, 2021
BACKGROUND AND OBJECTIVE: The objective of this study was to systematically review and meta-analyze the diagnostic accuracy of current machine learning classifiers for age-related macular degeneration (AMD). Artificial intelligence diagnostic algorit...
"Keep it simple, scholar": an experimental analysis of few-parameter segmentation networks for retinal vessels in fundus imaging.
International journal of computer assisted radiology and surgery
Apr 30, 2021
PURPOSE: With the recent development of deep learning technologies, various neural networks have been proposed for fundus retinal vessel segmentation. Among them, the U-Net is regarded as one of the most successful architectures. In this work, we sta...
Automatic glaucoma detection based on transfer induced attention network.
Biomedical engineering online
Apr 23, 2021
BACKGROUND: Glaucoma is one of the causes that leads to irreversible vision loss. Automatic glaucoma detection based on fundus images has been widely studied in recent years. However, existing methods mainly depend on a considerable amount of labeled...
Prediction of causative genes in inherited retinal disorder from fundus photography and autofluorescence imaging using deep learning techniques.
The British journal of ophthalmology
Apr 20, 2021
BACKGROUND/AIMS: To investigate the utility of a data-driven deep learning approach in patients with inherited retinal disorder (IRD) and to predict the causative genes based on fundus photography and fundus autofluorescence (FAF) imaging.
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