AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Diabetic Retinopathy

Showing 21 to 30 of 441 articles

Clear Filters

Cost-Saving Data-Driven Diabetic Retinopathy Prediction via a Sampling-Empowered Incremental Learning Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Diabetic retinopathy (DR) is a serious complication of diabetes that can lead to vision impairment or even blindness if not detected and treated in the early stage. Recently, leveraging the electronic health records (EHR) data, machine learning-based...

Diabetic retinopathy detection via deep learning based dual features integrated classification model.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundThe primary recognition of diabetic retinopathy (DR) is a pivotal requirement to prevent blindness and vision impairment. This deadly condition is identified by highly qualified professionals by examining colored retinal images.ObjectiveThe...

MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification.

Medical image analysis
Diabetic retinopathy (DR), the leading cause of vision loss among diabetic adults worldwide, underscores the importance of early detection and timely treatment using fundus images to prevent vision loss. However, existing deep learning methods strugg...

Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms.

European journal of medical research
OBJECTIVES: This study aimed to identify risk factors for diabetic retinopathy (DR) and develop machine learning (ML)-based predictive models using routine laboratory data in patients with type 2 diabetes mellitus (T2DM).

Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.

Biomedical engineering online
BACKGROUND: In recent years, artificial intelligence and machine learning algorithms have been used more extensively to diagnose diabetic retinopathy and other diseases. Still, the effectiveness of these methods has not been thoroughly investigated. ...

A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and classification.

Scientific reports
Diabetic Retinopathy (DR) is a serious condition affecting diabetes people caused by hemorrhage in the light-sensitive retinal area. DR sufferers should receive urgent therapy to avoid vision loss. The intelligent medical diagnosis system for DR is e...

Artificial Intelligence Versus Rules-Based Approach for Segmenting NonPerfusion Area in a DRCR Retina Network Optical Coherence Tomography Angiography Dataset.

Investigative ophthalmology & visual science
PURPOSE: Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable scre...

D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography.

Journal of medical systems
Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deterioration of eyesight. Fundus Fluorescein Angiography (FFA), recognized as the gold standard for diagnosing DR, effectively reveals abnormalities in retina...

Investigation and validation of genes associated with endoplasmic reticulum stress in diabetic retinopathy using various machine learning algorithms.

Experimental eye research
BACKGROUND: Diabetic retinopathy (DR) is a common complication of diabetes, with Endoplasmic reticulum stress (ERS) playing a key role in cellular adaptation, injury, or apoptosis, impacting disease pathology. This study aimed to identify early diagn...