Diabetes

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 to bias. This study aimed to identify regional differences in the diabetes burden on a universal level from the perspective of people with diabetes.

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 and poor acquisition continuity. In response to these shortcomings, we propose a multi-parameter fusion non-invasive blood glucose detection method that combines machine learning and photoplethysmography (PPG) signal feature parameter analysis.

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 explored the possibility of a relationship among diab...

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 ophthalmoscopy (AOSLO) images is crucial for identifying MA...

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 multi-factored and chronic disease nature of diabe...

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 struggle to practise it effectively. Most studies...

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 essential for patients with diabetes to prevent blin...

Collaborative Deep Learning for Privacy Preserving Diabetic Retinopathy Detection.

Convolutional Neural Networks (CNNs) are an emerging research area for detection of Diabetic Retinopathy (DR) development in fundus images with highly...

Automatic Wound Type Classification with Convolutional Neural Networks.

Chronic wounds are ulcerations of the skin that fail to heal because of an underlying condition such as diabetes mellitus or venous insufficiency. The...

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, patient's demographics and comorbidities. We examined ...

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

Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). H...

A Machine Learning-Based Predictive Model to Identify Patients Who Failed to Attend a Follow-up Visit for Diabetes Care After Recommendations From a National Screening Program.

OBJECTIVE: Reportedly, two-thirds of the patients who were positive for diabetes during screening failed to attend a follow-up visit for diabetes care...

Characterization of Type 2 Diabetes Using Counterfactuals and Explainable AI.

Type 2 diabetes mellitus is a metabolic disorder of glucose management, whose prevalence is increasing inexorably worldwide. Adherence to therapies, a...

SYSTEMATIC CORRELATION OF CENTRAL SUBFIELD THICKNESS WITH RETINAL FLUID VOLUMES QUANTIFIED BY DEEP LEARNING IN THE MAJOR EXUDATIVE MACULAR DISEASES.

PURPOSE: To investigate the correlation of volumetric measurements of intraretinal (IRF) and subretinal fluid obtained by deep learning and central re...

Assessing the Value of Artificial Intelligence in Retinopathy of Prematurity Screening-Be Specific With Regard to the Data.

Commentary: Deep learning in retinopathy of prematurity: Where do we stand?

CRISP: a deep learning architecture for GC × GC-TOFMS contour ROI identification, simulation and analysis in imaging metabolomics.

Two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) provides a large amount of molecular information from biological s...

Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion.

Feature representation and discriminative learning are proven models and technologies in artificial intelligence fields; however, major challenges for...

[Study on noninvasive blood glucose detection method using the near-infrared light based on particle swarm optimization and back propagation neural network].

Most of the existing near-infrared noninvasive blood glucose detection models focus on the relationship between near-infrared absorbance and blood glu...

Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning.

IMPORTANCE: Anomaly detectors could be pursued for retinal diagnoses based on artificial intelligence systems that may not have access to training exa...