AIMC Topic: Diabetic Retinopathy

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Real-World Evaluation of AI-Driven Diabetic Retinopathy Screening in Public Health Settings: Validation and Implementation Study.

JMIR medical informatics
BACKGROUND: Artificial intelligence (AI) algorithms offer an effective solution to alleviate the burden of diabetic retinopathy (DR) screening in public health settings. However, there are challenges in translating diagnostic performance and its appl...

MAFNet: A novel adaptive multi-scale model for fine-grained grading of diabetic retinopathy.

Scientific reports
Diabetic Retinopathy (DR) is a leading cause of blindness worldwide, and its early detection and accurate grading play a crucial role in clinical intervention. To address the dual limitations of existing methods in multi-scale lesions feature fusion ...

Disorganization of retinal inner layers as an optical coherence tomography biomarker in diabetic retinopathy: A review.

Indian journal of ophthalmology
Diabetic retinopathy is a leading cause of vision impairment globally. Disorganization of the retinal inner layers (DRIL), detected via optical coherence tomography, has emerged as a potential biomarker of disease severity and visual prognosis. This ...

Diabetic retinal disease.

Nature reviews. Disease primers
Diabetic retinopathy is a complication of diabetes mellitus that is clinically characterized by changes in retinal microvasculature. Diabetic retinopathy is now better defined as diabetic retinal disease (DRD), as diabetes mellitus affects not only t...

Development and validation of a deep learning model for early detection and screening of diabetic retinopathy.

BMC medical informatics and decision making
Early diagnosis and screening of diabetic retinopathy (DR) are crucial for reducing medical burdens and conserving healthcare resources. This study introduces an advanced AI-assisted recognition system designed to enhance the detection of DR lesions ...

Integrating non-linear radon transformation for diabetic retinopathy grading.

Scientific reports
Diabetic retinopathy is a serious ocular complication that poses a significant threat to patients' vision and overall health. Early detection and accurate grading are essential to prevent vision loss. Current automatic grading methods rely heavily on...

Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification.

Scientific reports
Eye diseases can affect vision and well-being, so early, accurate diagnosis is crucial to prevent serious impairment. Deep learning models have shown promise for automating the diagnosis of eye diseases from images. However, current methods mostly us...

Human expert grading versus automated quantification of fluid volumes in nAMD, DME and BRVO.

Scientific reports
This study compared an automated deep learning algorithm with certified human graders from the Vienna Reading Center (VRC) in identifying intra- (IRF) and subretinal fluid (SRF) in OCT scans of patients treated for neovascular age-related macular deg...

Diabetic retinopathy classification using a multi-attention residual refinement architecture.

Scientific reports
Diabetic Retinopathy (DR) is a complication caused by diabetes that can destroy the retina, leading to blurred vision and even blindness. We propose a multi-attention residual refinement architecture that enhances conventional CNN performance through...

AI-Assisted identification of sex-specific patterns in diabetic retinopathy using retinal fundus images.

PloS one
Diabetic retinopathy (DR) is a microvascular complication of diabetes that can lead to blindness if left untreated. Regular monitoring is crucial for detecting early signs of referable DR, and the progression to moderate to severe non-proliferative D...