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Retinal Diseases

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Trends in the Prevalence of Common Retinal and Optic Nerve Diseases in China: An Artificial Intelligence Based National Screening.

Translational vision science & technology
PURPOSE: Retinal and optic nerve diseases have become the primary cause of irreversible vision loss and blindness. However, there is still a lack of thorough evaluation regarding their prevalence in China.

A fusion of deep neural networks and game theory for retinal disease diagnosis with OCT images.

Journal of X-ray science and technology
Retinal disorders pose a serious threat to world healthcare because they frequently result in visual loss or impairment. For retinal disorders to be diagnosed precisely, treated individually, and detected early, deep learning is a necessary subset of...

Deep Learning Performance of Ultra-Widefield Fundus Imaging for Screening Retinal Lesions in Rural Locales.

JAMA ophthalmology
IMPORTANCE: Retinal diseases are the leading cause of irreversible blindness worldwide, and timely detection contributes to prevention of permanent vision loss, especially for patients in rural areas with limited medical resources. Deep learning syst...

Deep learning for precision medicine: Guiding laser therapy in ischemic retinal diseases.

Cell reports. Medicine
In this issue of Cell Reports Medicine, Zhao and colleagues report a multi-tasking artificial intelligence system that can assist the whole process of fundus fluorescein angiography (FFA) imaging and reduce the reliance on retinal specialists in FFA ...

Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot Learning.

Biomedical and environmental sciences : BES
OBJECTIVE: To develop a few-shot learning (FSL) approach for classifying optical coherence tomography (OCT) images in patients with inherited retinal disorders (IRDs).

Deep Learning-Based System for Disease Screening and Pathologic Region Detection From Optical Coherence Tomography Images.

Translational vision science & technology
PURPOSE: This study was designed to apply deep learning models in retinal disease screening and lesion detection based on optical coherence tomography (OCT) images.

SRV-GAN: A generative adversarial network for segmenting retinal vessels.

Mathematical biosciences and engineering : MBE
In the field of ophthalmology, retinal diseases are often accompanied by complications, and effective segmentation of retinal blood vessels is an important condition for judging retinal diseases. Therefore, this paper proposes a segmentation model fo...

A Deep Learning Method for Automatic Identification of Drusen and Macular Hole from Optical Coherence Tomography.

Studies in health technology and informatics
Deep Learning methods have become dominant in various fields of medical imaging, including ophthalmology. In this preliminary study, we investigated a method based on Convolutional Neural Network for the identification of drusen and macular hole from...

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.

Translational vision science & technology
PURPOSE: Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learni...

A Weakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images.

Translational vision science & technology
PURPOSE: The purpose of this study was to design an automated algorithm that can detect fluorescence leakage accurately and quickly without the use of a large amount of labeled data.