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Diabetic Retinopathy

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Deep learning for ultra-widefield imaging: a scoping review.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: This article is a scoping review of published and peer-reviewed articles using deep-learning (DL) applied to ultra-widefield (UWF) imaging. This study provides an overview of the published uses of DL and UWF imaging for the detection of opht...

Automated image curation in diabetic retinopathy screening using deep learning.

Scientific reports
Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require curation, a laborious task to perform manually. We developed and validated single and multi-output ...

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy.

Disease markers
The eye is one of the most important organs of the human body. Eye diseases are closely related to other systemic diseases, both of which influence each other. Numerous systemic diseases lead to special clinical manifestations and complications in th...

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach.

Sensors (Basel, Switzerland)
The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with ...

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs.

BioMed research international
Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings publi...

Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model.

Computational intelligence and neuroscience
In today's world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may ...

Optimized convolution neural network based multiple eye disease detection.

Computers in biology and medicine
World health organization (WHO) reports around 2.2 billion people in the world as visually challenged which is mostly due to the age-related eye diseases such as age-related macular degeneration (AMD), cataract, diabetic retinopathy (DR) and glaucoma...

Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning.

Journal of digital imaging
Automatic segmentation and measurement of the choroid layer is useful in studying of related fundus diseases, such as diabetic retinopathy and high myopia. However, most algorithms are not helpful for choroid layer segmentation due to its blurred bou...

A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique.

Medical & biological engineering & computing
Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medic...

Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis.

European journal of ophthalmology
PURPOSE: Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection.