AIMC Topic: Diabetic Retinopathy

Clear Filters Showing 11 to 20 of 502 articles

Non-linear association between Life's Essential 8 and diabetic retinopathy: mediating role of depression in US adults with diabetes.

BMC public health
BACKGROUND: Life's Essential 8 (LE8) is a comprehensive cardiovascular health (CVH) metric that is associated with chronic diseases. This study aimed to investigate the association between LE8 and diabetic retinopathy (DR) and the mediating role of d...

Privacy preservation in diabetic disease prediction using federated learning based on efficient cross stage recurrent model.

Scientific reports
Diabetic retinopathy (DR) is a major problemfor the diabetes patients that makes a serious threat to vision and causes the irreversible blindness if not diagnosed and treated early. Conventional deep learning-based approaches designed for DR detectio...

Plasma multi-omics and machine learning reveal predictive biomarkers for type 2 diabetes and retinopathy in Qatar biobank cohort.

Journal of translational medicine
BACKGROUND: Type 2 diabetes (T2D) and its vascular complications, including diabetic retinopathy (DR), are escalating in prevalence globally, with disproportionately high prevalence in Middle Eastern populations, where genetic predispositions and lif...

Evaluating trustworthiness in AI-Based diabetic retinopathy screening: addressing transparency, consent, and privacy challenges.

BMC medical ethics
BACKGROUND: Artificial intelligence (AI) offers significant potential to drive advancements in healthcare; however, the development and implementation of AI models present complex ethical, legal, social, and technical challenges, as data practices of...

Enhanced performance in automated diabetic retinopathy diagnosis achieved through Voronoi diagrams and artificial intelligence.

Scientific reports
Diabetic retinopathy (DR), a serious eye condition in diabetic patients, requires early and precise detection for effective treatment. Late diagnosis and poor blood sugar control exacerbate this condition, highlighting the need for improved diagnosti...

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...

Diagnostics of diabetic retinopathy based on fundus photos using machine learning methods with advanced feature engineering algorithms.

Scientific reports
Diabetes is one of the main diseases posing a threat to healthcare systems. One of the complications of diabetes is diabetic retinopathy, which, if left untreated, can lead to serious consequences such as blindness. Early detection of this disease is...

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...

Diabetic retinopathy as the primary predictor of mild cognitive impairment in type 2 diabetes: Insights from machine learning models.

PloS one
Mild cognitive impairment (MCI) is a significant and increasingly recognized problem in individuals with type 2 diabetes mellitus (T2DM). This study aims to develop a machine-learning model to predict MCI in patients with T2DMThe dataset was obtained...

Ratio of haemorrhagic area to retinal area as a novel indicator for AI-based screening of diabetic retinopathy in type 2 diabetes: a community-based cross-sectional study.

BMJ open
BACKGROUND: The application of artificial intelligence (AI) technology in the screening of diabetic retinopathy (DR) has made significant strides. However, there remains a lack of comprehensive validation and evaluation of AI-derived quantitative ind...