Endocrinology

Diabetes

Latest AI and machine learning research in diabetes for healthcare professionals.

2,607 articles
Stay Ahead - Weekly Diabetes research updates
Subscribe
Browse Categories
Showing 1471-1491 of 2,607 articles
DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images.

Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and follow up of ...

Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records.

OBJECTIVE: We analyzed data from inpatients with diabetes admitted to a large university hospital to...

Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study.

BACKGROUND: Deep learning is a novel machine learning technique that has been shown to be as effecti...

Artificial Intelligence: The Future for Diabetes Care.

Artificial intelligence (AI) is a fast-growing field and its applications to diabetes, a global pand...

Artificial Intelligence and Digital Tools: Future of Diabetes Care.

Diabetes mellitus has become a global threat, especially in the emerging economies. In the United St...

Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening.

PURPOSE: To develop a deep learning image assessment software VeriSee™ and to validate its accuracy ...

Microaneurysms detection in color fundus images using machine learning based on directional local contrast.

BACKGROUND: As one of the major complications of diabetes, diabetic retinopathy (DR) is a leading ca...

Ensemble Deep Learning for Diabetic Retinopathy Detection Using Optical Coherence Tomography Angiography.

PURPOSE: To evaluate the role of ensemble learning techniques with deep learning in classifying diab...

Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke.

Convolutional Neural Networks (CNNs) have become a prominent method of AI implementation in medical ...

Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems.

PURPOSE: We sought to assess the performance of deep learning approaches for differentiating nonglau...

Limits of trust in medical AI.

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advanceme...

Deep Learning Classification for Diabetic Foot Thermograms.

According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalen...

Modeling the Research Landscapes of Artificial Intelligence Applications in Diabetes (GAP).

The rising prevalence and global burden of diabetes fortify the need for more comprehensive and effe...

Diabetic retinopathy and ultrawide field imaging.

The introduction of ultrawide field imaging has allowed the visualization of approximately 82% of th...

Lessons Learned About Autonomous AI: Finding a Safe, Efficacious, and Ethical Path Through the Development Process.

Artificial intelligence (AI) describes systems capable of making decisions of high cognitive complex...

A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography.

Conventional glucose monitoring methods for the growing numbers of diabetic patients around the worl...

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms.

Chronic diseases represent a serious threat to public health across the world. It is estimated at ab...

NFN+: A novel network followed network for retinal vessel segmentation.

In the early diagnosis of diabetic retinopathy, the morphological attributes of blood vessels play a...

Browse Categories