Diabetes

Barriers and Facilitators to the Implementation of Artificial Intelligence Enabled Diabetes Interventions in Lower-Middle-Income Countries: A Systematic Review Protocol

Diabetes represents an emerging global health crisis, with lower-middle-income countries experiencing a fast growth in prevalence. Diabetes care in these regions often faces significant challenges, including inadequate healthcare infrastructure, limited financial resources, a shortage of trained healthcare personnel, and a dual burden of communicable and non-communicable diseases. Artificial intel...

Artificial Intelligence-Driven Innovations in Diabetes Care and Monitoring

This study explores Artificial Intelligence (AI)’s transformative role in diabetes care and monitoring, focusing on innovations that optimize patient outcomes. AI, particularly machine learning and deep learning, significantly enhances early detection of complications like diabetic retinopathy and improves screening efficacy. The methodology employs a bibliometric analysis using Scopus, VOSviewer,...

Artificial Intelligence for Early Detection and Prognosis Prediction of Diabetic Retinopathy

This review explores the transformative role of artificial intelligence (AI) in the early detection and prognosis prediction of diabetic retinopathy (...

Predicting Inpatient Risk of Mortality in Diabetic Patients Using Administrative Data and Machine Learning: An External Validation Study Using SPARCS

To evaluate whether machine learning models trained solely on administrative and demographic data can predict inpatient APR Risk of Mortality in diabe...

Enhancing Fairness in Diabetes Prediction Systems through Smart User Interface Design

Artificial intelligence (AI) in chronic disease prediction often exhibits algorithmic biases, hindering equitable healthcare delivery. This study aims...

Multiple instance learning using pathology foundation models effectively predicts kidney disease diagnosis and clinical classification

Histological analysis of kidney biopsies is crucial in diagnosing kidney diseases and predicting clinical outcomes. Recently developed pathology found...

Daily Rhythms in Blood Glucose: Time-of-Day Forecasts in Type 2 Diabetes

Accurate and interpretable forecasting of blood glucose levels is critical for effective manage- ment of Type 2 diabetes. While complex machine learni...

Application of Machine Learning Approaches to Develop Predictive Models for Diabetes and Hypertension among Bangladesh Adults

With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs), including hypertension and diabetes, pose signi...

Hybrid Fuzzy Logic and Logistic Regression Model with Recursive Feature Elimination for Enhanced Prediction and Clinical Decision Support in Type 2 Diabetes Mellitus Among Adults Aged 35 to 45

This paper presents a hybrid model combining fuzzy logic, recursive feature elimination (RFE), and logistic regression to predict type 2 diabetes mell...

Integrating Bioinformatics and Machine Learning to Identify Mitochondria-Related Biomarkers and Their Association with Immune Infiltration in BK polyomavirus-associated nephropathy

BK polyomavirus-associated nephropathy (BKPyVAN) is a serious complication of kidney transplantation. Numerous kidney diseases such as BKPyVAN have be...

Empowering digital health management with on-device large language models for glucose prediction

Long-term management of chronic diseases such as diabetes is increasingly based on wearable technologies, particularly continuous glucose monitoring (...

Myocardial Native T1 Mapping in the German National Cohort (NAKO): Associations with Age, Sex, and Cardiometabolic Risk Factors

In cardiovascular magnetic resonance (CMR), myocardial native T1 mapping enables quantitative, non-invasive tissue characterization and is sensitive t...

Genetic Profiling and Early Detection of Type 2 Diabetes Subtypes through Sex-Stratified GWAS and Explainable AI

Type 2 diabetes (T2D) is a complex and clinically heterogeneous disease. Although clustering approaches have defined clinical subtypes, their genetic ...

Genomics reveals eleven obesity endotypes with distinct biological and phenotypic signatures

Obesity, a leading global risk factor for cardiometabolic conditions, arises from multifaceted and biologically complex mechanisms1,2. To elucidate th...

Serum Exosomal Multi-Omic Signatures Stratify Glucose Tolerance in Cystic Fibrosis and Reveal Partial Therapeutic Reprogramming by CFTR Modulators

Cystic fibrosis-related diabetes (CFRD) affects up to 60% of adults with CF and contributes to poorer clinical outcomes, including accelerated lung de...

MedAdhereAI: An Interpretable Machine Learning Pipeline for Predicting Medication Non-Adherence in Chronic Disease Patients Using Real-World Refill Data

Medication non-adherence remains a significant challenge in managing chronic conditions like diabetes and hypertension, leading to increased morbidity...

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study

Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary art...

Quantification of Optical Coherence Tomography Features in >3500 Patients with Inherited Retinal Disease Reveals Novel Genotype-Phenotype Associations

To quantify spectral-domain optical coherence tomography (SD-OCT) images cross-sectionally and longitudinally in a large cohort of molecularly charact...

A conversational artificial intelligence based web application for medical conversations: a prototype for a chatbot

Artificial Intelligence (AI) has evolved through various trends, with different subfields gaining prominence over time. Currently, Conversational Arti...

High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort

Synthetic data generated using generative models trained on real clinical data offers a promising solution to privacy concerns in health research. How...