Diabetes

Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning.

In the U.S., over a third of adults are pre-diabetic, with 80% unaware of their status. This underlines the need for better glucose monitoring to prevent type 2 diabetes and related heart diseases. Existing wearable glucose monitors are limited by the lack of models trained on small datasets, as collecting extensive glucose data is often costly and impractical. Our study introduces a novel machine...

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

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 DR prediction becomes a promising research direction to achieve timely diagnosis of DR. In practice, the EHR database usually increases periodically,...

Exploring Biomarker Relationships in Both Type 1 and Type 2 Diabetes Mellitus Through a Bayesian Network Analysis Approach

Understanding the complex relationships of biomarkers in diabetes is pivotal for advancing treatment strategies, a pressing need in diabetes researc...

Guided Context Gating: Learning to leverage salient lesions in retinal fundus images

Effectively representing medical images, especially retinal images, presents a considerable challenge due to variations in appearance, size, and con...

Attention Networks for Personalized Mealtime Insulin Dosing in People with Type 1 Diabetes

Calculating mealtime insulin doses poses a significant challenge for individuals with Type 1 Diabetes (T1D). Doses should perfectly compensate for e...

Fair by design: A sociotechnical approach to justifying the fairness of AI-enabled systems across the lifecycle

Fairness is one of the most commonly identified ethical principles in existing AI guidelines, and the development of fair AI-enabled systems is requ...

Deep Learning to Predict Glaucoma Progression using Structural Changes in the Eye

Glaucoma is a chronic eye disease characterized by optic neuropathy, leading to irreversible vision loss. It progresses gradually, often remaining u...

Detection of Acetone as a Gas Biomarker for Diabetes Based on Gas Sensor Technology

With the continuous development and improvement of medical services, there is a growing demand for improving diabetes diagnosis. Exhaled breath anal...

Assessing the Efficacy of Synthetic Optic Disc Images for Detecting Glaucomatous Optic Neuropathy Using Deep Learning.

PURPOSE: Deep learning architectures can automatically learn complex features and patterns associated with glaucomatous optic neuropathy (GON). Howeve...

Neural-Net Artificial Pancreas: A Randomized Crossover Trial of a First-in-Class Automated Insulin Delivery Algorithm.

Automated insulin delivery (AID) is now integral to the clinical practice of type 1 diabetes (T1D). The objective of this pilot-feasibility study was...

Machine learning to predict type 1 diabetes in children.

Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm.

BACKGROUND: Children presenting to primary care with suspected type 1 diabetes should be referred immediately to secondary care to avoid life-threaten...

Comparing the accuracy of four machine learning models in predicting type 2 diabetes onset within the Chinese population: a retrospective study.

OBJECTIVE: To evaluate the effectiveness of machine learning (ML) models in predicting 5-year type 2 diabetes mellitus (T2DM) risk within the Chinese ...

[Personalized glycemic management for patients with diabetic ketoacidosis based on machine learning].

OBJECTIVE: To explore the optimal blood glucose-lowering strategies for patients with diabetic ketoacidosis (DKA) to enhance personalized treatment ef...

Biomarker signatures associated with ageing free of major chronic diseases: results from a population-based sample of the EPIC-Potsdam cohort.

BACKGROUND: A number of biomarkers denoting various pathophysiological pathways have been implicated in the aetiology and risk of age-related diseases...

Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

PURPOSE: Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This st...

Is Artificial Intelligence for Retinopathy of Prematurity Ready to Go?

A deep learning system for predicting time to progression of diabetic retinopathy.

StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides.

MOTIVATION: Diabetes is a chronic metabolic disorder that has been a major cause of blindness, kidney failure, heart attacks, stroke, and lower limb a...

Worldwide productivity and research trend of publications concerning intestinal polyps: A bibliometric study.

There is a significant relationship between intestinal polyps and colorectal cancer, and in recent years, research on intestinal polyps has been rapid...