Diabetes

Automating wound assessment: convolutional neural network-based mobile application for SINBAD classification system.

INTRODUCTION: Diabetic foot ulcer (DFU) assessment using the SINBAD system is essential for clinical decision-making but often limited by access to specialists. This study presents a mobile application powered by a lightweight Convolutional Neural Networks (MobileNetV3 Small) to automate DFU classification. METHODS: A dataset of 996 clinician-labeled DFU images was used to train the model to class...

Development and validation of a machine learning model to predict functional outcomes in patients with recent small subcortical infarction.

OBJECTIVE: A substantial proportion of patients (12 %-25 %) with recent small subcortical infarction (RSSI) suffer poor functional outcomes at 3 months. Despite the identification of prognostic factors, a significant gap exists in predictive modeling. This study aimed to develop and validate machine learning models to accurately predict 3-month functional status in this patient population. METHODS...

Elevated Retinal Neovascularization on Widefield Optical Coherence Tomography Angiography Predicts Complications in High-Risk Proliferative Diabetic Retinopathy.

PURPOSE: To determine whether retinal neovascularization (RNV) metrics derived from single-shot widefield swept-source OCT angiography (SS-OCTA) predi...

Integrative machine learning reveals hidden and emerging co-regulatory gene networks for multi-phase glioblastoma outcome prediction.

BACKGROUND: Glioblastoma (GBM) is a highly prevalent and aggressive type of brain tumor characterized by profound molecular complexity and poor progno...

Protector role of muscle quality against diabetes in non-elderly U.S. adults.

BACKGROUND: Since 1980, the number of people with diabetes has doubled globally, a figure expected to reach 783 million by 2045. The Muscle Quality In...

Integrated analysis of single-cell and bulk RNA-seq data identifies NR4A1-associated macrophages in immunoglobulin A nephropathy.

Immunoglobulin A nephropathy (IgAN), the most prevalent primary glomerulonephritis worldwide, is characterized by chronic renal inflammation and progr...

Machine learning-based prediction of large-for-gestational-age neonates in diabetic and non-diabetic pregnancies.

OBJECTIVE: This study determines whether a machine-learning model integrating sonographic biometry with maternal clinical parameters improves predicti...

Relationship between cardiometabolic index and worsening renal function in T2DM: Insights from machine learning, growth mixture modelling, and development of a web-based risk prediction tool.

AIM: Worsening renal function (WRF) is a common and serious complication of type 2 diabetes mellitus (T2DM), contributing to adverse clinical outcomes...

Neuroinflammation and insulin resistance in major depression and bipolar disorder: Implications for clinical trials evaluating immunometabolic targeted therapies.

Bipolar disorder (BD) and major depressive disorder (MDD) are highly prevalent, disabling psychiatric illnesses marked by substantial heterogeneity an...

Enhancing deep learning telemedicine for retinopathy of prematurity: current evidence and opportunities for improvement.

Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography.

PURPOSE: To conduct a comprehensive systematic evaluation of federated learning (FL) strategies for multi-disease retinal classification using OCT ang...

Pathway to precision: machine learning and bioinformatics in diabetes gene expression studies.

BACKGROUND: Diabetes mellitus is a metabolic disorder; understanding the pathogenic mechanisms underlying diabetes is crucial. Analyzing biomarkers, s...

The efficacy of CGM driven machine learning algorithms in predicting hypoglycemia in patients with T1DM: a systematic review and meta-analysis.

PURPOSE: An important function of continuous glucose monitoring (CGM) is to alert individuals with type one diabetes mellitus (T1DM) to impending hypo...

Comments on: "Artificial intelligence improves survival decision-making in diabetic emergencies: A cross-sectional study".

Literature-informed ensemble machine learning for three-year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the PSMMC NephraRisk model.

INTRODUCTION: Diabetic kidney disease (DKD) and diabetic nephropathy (DN) affect around 40% of diabetic patients but lack accurate risk prediction too...

Synergistic impact of insulin resistance and hepatic fibrosis on cardiovascular events in patients with coronary artery disease: A machine learning subtyping and interaction analysis.

BACKGROUND AND AIMS: Insulin resistance (IR) and hepatic fibrosis are significant yet underexplored synergistic risk factors for cardiovascular events...

Automated Feature Segmentation of Ultra-Widefield OCT Images.

OBJECTIVE OR PURPOSE: To develop a lightweight neural network for automated cross-sectional and en face segmentation of ultra-widefield (UWF) OCT imag...

AI-powered literature mining reveals the therapeutic significance of GLP-1 receptor: Simulation of natural agonist candidates based on molecular dynamics.

Glucagon-like peptide-1 (GLP-1), a pivotal incretin hormone modulating glycemic homeostasis, has emerged as a clinically validated target for the trea...

Explainable machine learning-driven predictive modelling of incident cognitive impairment in individuals with early-stage cardiovascular-kidney-metabolic syndrome: Insights from a longitudinal CHARLS cohort study.

AIMS: While cardiovascular-kidney-metabolic (CKM) syndrome has been recognised as a continuum of interconnected metabolic, renal, and cardiovascular d...

Clinical utility of an AI-based retinal imaging model for cardiovascular risk prediction in hypertensive retinopathy.

OBJECTIVE: This study presents an independent clinical evaluation of Dr.Noon CVD, a commercially developed artificial intelligence (AI)-based retinal ...