Diabetes

Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme.

BACKGROUND/AIMS: National guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to referable DR beyond DR grading, and the potential impact on assigned screening intervals, within the Scottish screening programme.

Smart diabetic foot ulcer scoring system.

Current assessment methods for diabetic foot ulcers (DFUs) lack objectivity and consistency, posing a significant risk to diabetes patients, including the potential for amputations, highlighting the urgent need for improved diagnostic tools and care standards in the field. To address this issue, the objective of this study was to develop and evaluate the Smart Diabetic Foot Ulcer Scoring System, S...

Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery.

BACKGROUND: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model...

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.

In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant c...

Machine Learning-Based Mortality Prediction in Chronic Kidney Disease among Heart Failure Patients: Insights and Outcomes from the Jordanian Heart Failure Registry.

Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on healthcare systems and adversely affects the qualit...

Longitudinal artificial intelligence-based deep learning models for diagnosis and prediction of the future occurrence of polyneuropathy in diabetes and prediabetes.

OBJECTIVE: The objective of this study was to develop artificial intelligence-based deep learning models and assess their potential utility and accura...

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for prec...

Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency.

Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treat...

Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial.

BACKGROUND: Prediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microv...

Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics.

Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have d...

Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous Specialist Confirmation.

PURPOSE: Diabetic retinopathy (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to ophthalmic care is...

Association between breastfeeding duration and diabetes mellitus in menopausal women: a machine-learning analysis using population-based retrospective study.

BACKGROUND: Breastfeeding resets insulin resistance caused by pregnancy however, studies on the association between breastfeeding and diabetes mellitu...

Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.

Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study ai...

Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.

BACKGROUND: Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved ...

Digital twins and artificial intelligence in metabolic disease research.

Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, w...

Research on disease diagnosis based on teacher-student network and Raman spectroscopy.

Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. T...

Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.

BACKGROUND/OBJECTIVES: Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonom...

Development and External Validation of a Multidimensional Deep Learning Model to Dynamically Predict Kidney Outcomes in IgA Nephropathy.

KEY POINTS: A dynamic model predicts IgA nephropathy prognosis based on deep learning. Longitudinal clinical data and deep learning improve predictive...

Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning.

BACKGROUND: Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates sing...

Risk prediction model of metabolic syndrome in perimenopausal women based on machine learning.

INTRODUCTION: Metabolic syndrome (MetS) is considered to be an important parameter of cardio-metabolic health and contributing to the development of a...