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Retina

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Deep compressed multichannel adaptive optics scanning light ophthalmoscope.

Science advances
Adaptive optics scanning light ophthalmoscopy (AOSLO) reveals individual retinal cells and their function, microvasculature, and micropathologies in vivo. As compared to the single-channel offset pinhole and two-channel split-detector nonconfocal AOS...

Can off-the-shelf visual large language models detect and diagnose ocular diseases from retinal photographs?

BMJ open ophthalmology
BACKGROUND: The advent of generative artificial intelligence has led to the emergence of multiple vision large language models (VLLMs). This study aimed to evaluate the capabilities of commonly available VLLMs, such as OpenAI's GPT-4V and Google's Ge...

Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis.

Frontiers in endocrinology
OBJECTIVE: To systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).

A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging.

Sensors (Basel, Switzerland)
Stroke is a leading cause of death and disability in developed countries. We validated an AI-based prediction model for incident stroke using sensors such as fundus cameras and ophthalmoscopes for retinal images, along with socio-demographic data and...

A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images.

Scientific reports
Recent advancements in deep learning have significantly impacted medical image processing domain, enabling sophisticated and accurate diagnostic tools. This paper presents a novel hybrid deep learning framework that combines convolutional neural netw...

Diabetic retinopathy detection based on mobile maxout network and weber local descriptor feature selection using retinal fundus image.

Scientific reports
Retinal screening provides for earlier detection of diabetic retinopathy (DR) as well as prompt diagnosis. Recognizing DR utilizing color fundus imaging needs qualified specialists to know about the presence and significance of a few insignificant fe...

EffiViT: Hybrid CNN-Transformer for Retinal Imaging.

Computers in biology and medicine
The human eye is a vital sensory organ that is crucial for visual perception. The retina is the main component of the eye and is responsible for visual signals. Due to its characteristics, the retina can reveal the occurrence of ocular diseases. So, ...

Global-Local Transformer Network for Automatic Retinal Pathological Fluid Segmentation in Optical Coherence Tomography Images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: As a pivotal biomarker, the accurate segmentation of retinal pathological fluid such as intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED), was a critical task for diagnosis and treatme...

Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network.

Science advances
Vasculature morphology and hierarchy are essential for blood perfusion. Human retinal circulation is an intricate vascular system emerging and remerging at the optic nerve head (ONH). Tracing retinal vascular branching from ONH can allow detailed mor...

Non-proliferative diabetic retinopathy detection using Rosmarus Quagga optimized explainable generative meta learning based deep convolutional neural network model.

International ophthalmology
PURPOSE: Non-Proliferative Diabetic Retinopathy (NPDR) is a complication of diabetes disease where there is damage of the blood vessels in retina but with no signs of formation of new vessels. It is present in the earlier stages and therefore the con...