AIMC Topic: Diabetic Retinopathy

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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...

Deep learning segmentation of periarterial and perivenous capillary-free zones in optical coherence tomography angiography.

Journal of biomedical optics
SIGNIFICANCE: Automated segmentation of periarterial and perivenous capillary-free zones (CFZs) in optical coherence tomography angiography (OCTA) can significantly improve early detection and monitoring of diabetic retinopathy (DR), a leading cause ...

A Lesion-Fusion Neural Network for Multi-View Diabetic Retinopathy Grading.

IEEE journal of biomedical and health informatics
As the most common complication of diabetes, diabetic retinopathy (DR) is one of the main causes of irreversible blindness. Automatic DR grading plays a crucial role in early diagnosis and intervention, reducing the risk of vision loss in people with...

Latent space autoencoder generative adversarial model for retinal image synthesis and vessel segmentation.

BMC medical imaging
Diabetes is a widespread condition that can lead to serious vision problems over time. Timely identification and treatment of diabetic retinopathy (DR) depend on accurately segmenting retinal vessels, which can be achieved through the invasive techni...

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...

Machine Learning-Driven Identification of Hematological and Immunological Biomarkers for Predicting Proliferative Diabetic Retinopathy Progression.

Current eye research
PURPOSE: Proliferative Diabetic Retinopathy (PDR) is a severe complication of diabetes characterized by neovascularization and retinal detachment, leading to significant vision loss. This study investigates the predictive power of hematological and i...

SEM model analysis of diabetic patients' acceptance of artificial intelligence for diabetic retinopathy.

BMC medical informatics and decision making
AIMS: This study aimed to investigate diabetic patients' acceptance of artificial intelligence (AI) devices for diabetic retinopathy screening and the related influencing factors.

Optimizing diabetic retinopathy detection with electric fish algorithm and bilinear convolutional networks.

Scientific reports
Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, necessitating regular screenings to prevent its progression to severe stages. Manual diagnosis is labor-intensive and prone to inaccuracies, highlighting the need for automat...

The association of trimethylamine N-oxide with diabetic retinopathy Pathology: Insights from network toxicology and molecular docking analysis.

Experimental eye research
Trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite, has emerged as a potential contributor to diabetic retinopathy (DR) progression. However, its molecular mechanisms in DR remain unclear. This study integrates network toxicology and ...

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...