Endocrinology

Diabetes

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

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Machine learning-based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, marke...

Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.

For people with Type 1 diabetes (T1D), accurate blood glucose (BG) forecasting is crucial for the ef...

The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach.

INTRODUCTION: Epigenetic biomarkers are molecular indicators of epigenetic changes, and some studies...

Machine Learning-Assisted Portable Dual-Readout Biosensor for Visual Detection of Milk Allergen.

Beta-lactoglobulin (β-LG), the primary allergen in cow's milk, makes developing a rapid, sensitive, ...

Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.

BACKGROUND: Medication adherence plays a crucial role in determining the health outcomes of patients...

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and i...

User-Centered Prototype Design of a Health Care Robot for Treating Type 2 Diabetes in the Community Pharmacy: Development and Usability Study.

BACKGROUND: Technology can be an effective tool for providing health services and disease self-manag...

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

OBJECTIVES: This study aimed to identify risk factors for diabetic retinopathy (DR) and develop mach...

Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis.

OBJECTIVE: To systematically review and meta-analyze the effectiveness of deep learning algorithms a...

Dynamic glucose enhanced imaging using direct water saturation.

PURPOSE: Dynamic glucose enhanced (DGE) MRI studies employ CEST or spin lock (CESL) to study glucose...

Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection.

Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health....

Establishment and validation of a ResNet-based radiomics model for predicting prognosis in cervical spinal cord injury patients.

Cervical spinal cord injury (cSCI) poses a significant challenge due to the unpredictable nature of ...

Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation.

Ischemic stroke's complex pathophysiology demands therapeutic approaches targeting multiple pathways...

Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms.

In recent years, diabetes has become a global public health problem, and it is reported that the mig...

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