Diabetes

Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device.

Detecting glucose levels is crucial for diabetes patients as it enables timely and effective management, preventing complications and promoting overall health. In this endeavor, we have designed a novel, affordable point-of-care diagnostic device utilizing microfluidic principles, a smartphone camera, and established laboratory colorimetric methods for accurate glucose estimation. Our proposed mic...

Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers.

BACKGROUND: To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU).

Can Digital Technology Revolutionize Continuous Education in Diabetes Care?

Rapid technological advancements, such as artificial intelligence, wearable technologies, and telehealth with remote monitoring, are transforming cont...

A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.

BACKGROUND AND AIMS: To prevent end-stage renal disease caused by diabetic kidney disease, we created a predictive model for high-risk patients using ...

Unveiling the utility of artificial intelligence for prediction, diagnosis, and progression of diabetic kidney disease: an evidence-based systematic review and meta-analysis.

OBJECTIVE: The purpose of this study was to conduct a systematic investigation of the potential of artificial intelligence (AI) models in the predicti...

Machine learning algorithms using the inflammatory prognostic index for contrast-induced nephropathy in NSTEMI patients.

Inflammatory prognostic index (IPI), has been shown to be related with poor outcomes in cancer patients. We aimed to investigate the predictive role ...

Development and external validation of a machine learning model to predict diabetic nephropathy in T1DM patients in the real-world.

AIMS: Studies on machine learning (ML) for the prediction of diabetic nephropathy (DN) in type 1 diabetes mellitus (T1DM) patients are rare. This stud...

Explainable machine learning model for predicting the risk of significant liver fibrosis in patients with diabetic retinopathy.

BACKGROUND: Diabetic retinopathy (DR), a prevalent complication in patients with type 2 diabetes, has attracted increasing attention. Recent studies h...

Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accu...

Predictive modeling of gestational weight gain: a machine learning multiclass classification study.

BACKGROUND: Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to variou...

GKE-TUNet: Geometry-Knowledge Embedded TransUNet Model for Retinal Vessel Segmentation Considering Anatomical Topology.

Automated retinal vessel segmentation is crucial for computer-aided clinical diagnosis and retinopathy screening. However, deep learning faces challen...

Circulating endothelial progenitor cells and inflammatory markers in type 1 diabetes after an acute session of aerobic exercise.

OBJECTIVE: To determine circulating endothelial progenitor cells (EPC) counts and levels of inflammatory markers in individuals with and without type ...

Enhancing the accuracy of blood-glucose tests by upgrading FTIR with multiple-reflections, quantum cascade laser, two-dimensional correlation spectroscopy and machine learning.

The accuracy of screening diabetes from non-diabetes is drastically enhanced by strategically upgrading the bench-marking infrared spectroscopy techni...

Multi-lesion segmentation guided deep attention network for automated detection of diabetic retinopathy.

Accurate multi-lesion segmentation together with automated grading on fundus images played a vital role in diagnosing and treating diabetic retinopath...

Screening of CAD-related secretory genes associated with type II diabetes based on comprehensive bioinformatics analysis and machine learning.

BACKGROUND: Type II diabetes mellitus (T2DM) is strongly linked with a heightened risk of coronary artery disease (CAD). Exploring biological targets ...

Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.

BACKGROUND: This study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients...

Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications.

Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDI...

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis.

The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Pres...

An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates.

This study aimed to construct a high-performance prediction and diagnosis model for type 2 diabetic retinopathy (DR) and identify key correlates of DR...

Advances in Imaging-Based Machine Learning and Therapeutic Technology in the Management of Retinal Diseases.

Retinal conditions like age-related macular degeneration (AMD), diabetic retinopathy, central serous chorioretinopathy (CSCR), and retinal vein occlus...