AIMC Topic: Retina

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DB-SegNet: optimized framework for glaucoma detection and optic structure segmentation from retinal fundus images.

Scientific reports
Glaucoma remains one of the primary causes of irreversible blindness, characterized by gradual damage to the optic nerve, which often goes undetected until advanced stages. Accurate and early diagnosis depends heavily on precise segmentation of the o...

Broad-spectrum eye disease classification using a deep learning-based tailored software lens.

PloS one
The early and accurate classification of eye diseases is essential for preventing irreversible visual impairment. This task can be performed by deep learning approaches that automatically classify retinal fundus images according to potential illnesse...

Benchmarking diffusion models against state-of-the-art architectures for OCT fluid biomarker segmentation.

PloS one
OBJECTIVES: Retinal diseases, major causes of vision impairment and blindness, are assessed using optical coherence tomography (OCT) scans. Automated report generation for retinal OCT scans, powered by deep learning, can help standardize interpretati...

Prediction of advanced chronic kidney disease through retinal fundus images by deep learning.

Scientific reports
This study was developed and evaluated deep learning model for detecting chronic kidney disease (CKD) by retinal fundus images. This study included 42,963 clinical visits from 17,442 patients who underwent retinal fundus examination between October 1...

A robust deep learning classifier for screening multiple retinal diseases on optical coherence tomography.

Scientific reports
Retinal diseases are among the leading causes of visual impairment worldwide, where timely diagnosis and management are critical to prevent irreversible vision loss and blindness, especially in regions with limited access to ophthalmologists. While a...

Multi-task deep learning framework combining CNN: vision transformers and PSO for accurate diabetic retinopathy diagnosis and lesion localization.

Scientific reports
Diabetic Retinopathy (DR) continues to be the leading cause of preventable blindness worldwide, and there is an urgent need for accurate and interpretable framework. A Multi View Cross Attention Vision Transformer (MVCAViT) framework is proposed in t...

Multi scale self supervised learning for deep knowledge transfer in diabetic retinopathy grading.

Scientific reports
Diabetic retinopathy is a leading cause of vision loss, necessitating early, accurate detection. Automated deep learning models show promise but struggle with the complexity of retinal images and limited labeled data. Due to domain differences, tradi...

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...

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...