Diabetes

Artificial intelligence in diabetes mellitus and endocrine diseases - what can we expect?

Deep learning automation of MEST-C classification in IgA nephropathy.

BACKGROUND: Although the MEST-C classification is among the best prognostic tools in immunoglobulin ...

Predicting Long-Term Type 2 Diabetes with Artificial Intelligence (AI): A Scoping Review.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that affects a significant portion o...

Deep learning-based multi-omics study reveals the polymolecular phenotypic of diabetic kidney disease.

Application of Supervised and Unsupervised Learning Approaches for Mapping Storage Conditions of Biopharmaceutical Product-A Case Study of Human Serum Albumin.

The stability of biopharmaceutical therapeutics over the storage period/shelf life has been a challe...

Deep learning-based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis.

Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. De...

Potential and Pitfalls of ChatGPT and Natural-Language Artificial Intelligence Models for Diabetes Education.

IGF-1 stabilizes goat sperm mitochondrial transmembrane potential and reduces dna fragmentation.

BACKGROUND: Antioxidant present in sperm cells protects them from oxidative damage. However, sperm a...

Advancing Diabetic Retinopathy Diagnosis: Leveraging Optical Coherence Tomography Imaging with Convolutional Neural Networks.

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes, necessitating early and ...

A knowledge-based decision support system for inferring supportive treatment recommendations for diabetes mellitus.

BACKGROUND: Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, ...

Artificial Intelligence in Efficient Diabetes Care.

Diabetes is a chronic disease that is not easily curable but can be managed efficiently. Artificial ...

Global diabetes burden: analysis of regional differences to improve diabetes care.

INTRODUCTION: The current evaluation processes of the burden of diabetes are incomplete and subject ...

Research on multi-parameter fusion non-invasive blood glucose detection method based on machine learning.

OBJECTIVE: Traditional blood glucose testing methods have several disadvantages, such as high pain a...

Clinical significance of hepatic function in Graves disease with type 2 diabetic mellitus: A single-center retrospective cross-sectional study in Taiwan.

Graves disease (GD) and type 2 diabetes mellitus (T2DM) both impair liver function; we therefore exp...

AOSLO-net: A Deep Learning-Based Method for Automatic Segmentation of Retinal Microaneurysms From Adaptive Optics Scanning Laser Ophthalmoscopy Images.

PURPOSE: Accurate segmentation of microaneurysms (MAs) from adaptive optics scanning laser ophthalmo...

Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying.

Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the ...

Identifying daily activities of patient work for type 2 diabetes and co-morbidities: a deep learning and wearable camera approach.

OBJECTIVE: People are increasingly encouraged to self-manage their chronic conditions; however, many...

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography.

PURPOSE: Reliable classification of referable and vision threatening diabetic retinopathy (DR) is es...

Collaborative Deep Learning for Privacy Preserving Diabetic Retinopathy Detection.

Convolutional Neural Networks (CNNs) are an emerging research area for detection of Diabetic Retinop...

Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques.

Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically col...

Treatment Prediction in the ICU Setting Using a Partitioned, Sequential Deep Time Series Analysis.

We developed a neural network architecture to evaluate the patient's state using temporal data, pati...