Primary Care

Self-Identified Age Cohorts and Personal Experience Sharing in Pseudonymous Online Spaces: Natural Language Processing Analysis of Reddit.

BACKGROUND: While older adults' social media use has been widely studied for its instrumental benefits, such as accessing health information or maintaining family ties, research has largely focused on identity-based platforms that mirror offline social networks, leaving pseudonymous, interest-driven environments such as Reddit underexplored. Although older adults actively participate in these spac...

Experiences and Perceived Influence of the Artificial Intelligence-Based Health Education Accurately Linking System (AI-HEALS) on Health Behaviors Among Patients With Type 2 Diabetes: Qualitative Study.

BACKGROUND: The management of type 2 diabetes requires sustained self-management across diet, physical activity, medication adherence, and blood glucose monitoring; however, maintaining these behaviors in daily life remains difficult for many patients. Artificial intelligence-enabled and mobile health interventions have shown promise in supporting diabetes education and self-management, but eviden...

Clinically interpretable nomogram incorporating radiomics and deep learning feature fusion from abdominal CT for preclinical type 2 diabetes.

OBJECTIVE: To develop and externally validate a multimodal artificial intelligence framework for opportunistic detection of preclinical type 2 diabete...

Exploring human GABA transporter 3 binders for epilepsy through quantum reactivity analysis, molecular dynamics, machine learning prediction, and network pharmacology.

CONTEXT: Drug-resistant epilepsy (DRE) remains a major therapeutic challenge, affecting millions of patients globally who do not respond to convention...

Methodological Approaches to and Reported Performance of Applications of Automated Machine Learning in Diabetes Risk Prediction: Rapid Review.

BACKGROUND: Type 2 diabetes (T2D) is a complex, chronic condition that imposes a substantial burden on health care systems. Prevention and early detec...

Multimodal Prediction of Periodontitis Using Root Exposure in Intraoral Images and Age.

INTRODUCTION AND AIMS: Despite advances in AI-based periodontitis screening, quantifiable and interpretable biomarkers from intraoral photographs rema...

Peripheral retinal haemorrhage density on ultra-widefield imaging as a novel biomarker for predicting diabetic retinopathy progression: a 2-year longitudinal study in Asians.

AIMS: To evaluate whether predominantly peripheral lesions (PPLs) and other imaging biomarkers on ultra-widefield (UWF) photography can predict the pr...

NerveAI- a machine learning algorithm for detection of nerve pain in the head and neck.

Nerve pain screening in patients with headache disorders requires specialized clinical knowledge that is frequently not readily accessible at the init...

Serum KIM-1 molecular early warning radar: SERS combined with artificial intelligence for accurate early diagnosis of chronic kidney disease.

Chronic kidney disease (CKD) has become a major global public health issue. Due to its insidious onset and atypical early symptoms, patients often fai...

Design and validation of renal stone detection using multi-architecture feature extraction with deep sequential learning model on axial computed tomography images.

Kidney stone disease is a significant public health threat, with its prevalence escalating due to evolving dietary habits, rising rates of obesity, ot...

Foundation for Artificial Intelligence-Driven Democratization of Healthcare Access.

INTRODUCTION: Healthcare systems worldwide face unprecedented challenges, including escalating costs, workforce shortages, and access disparities, whi...

Associations of Red Blood Cell Distribution Width-Derived Indicators and Their Longitudinal Dynamic Trajectories With Mortality Risk in Critically Ill Patients With Pulmonary Hypertension.

Red blood cell distribution width (RDW)-derived indicators have increasingly been recognized as biomarkers reflecting systemic inflammation and hemato...

Explainable retinal deep learning for cardiovascular risk stratification: a multiple modality analysis framework with vascular-centric interpretability and robustness.

Early non-invasive prediction of cardiovascular risk is an essential prerequisite for preventive medicine, especially in resource-limited settings. Re...

Deep learning-based automated segmentation and quantification of aortic arch calcification at chest radiograph.

BACKGROUND: Aortic arch calcification (AoAC) is an established independent predictor of coronary heart disease and broader cardiovascular outcomes. We...

A TyG-UHR-based machine learning model for screening lean MAFLD: development and external validation.

BACKGROUND: Lean metabolic dysfunction-associated fatty liver disease (MAFLD) is increasingly recognized but often goes unnoticed during health checku...

Cardiovascular Risk Stratification in Youth-Onset Type 2 Diabetes Using Machine Learning.

BACKGROUND: The rising incidence of youth-onset type 2 diabetes mellitus (T2DM), along with the risk of early cardiovascular complications, is concern...

Decoding Parkinson's progression: A multi-modal SuStaIn ensemble approach validated on real-world PPMI data.

BACKGROUND: Parkinson's disease (PD) exhibits substantial heterogeneity in clinical presentation and longitudinal progression, complicating prognosis,...

AI-Driven Discovery of an Allosteric Thrombin-Targeting Anticoagulant Peptide from Traditional Chinese Medicine Dilong (Pheretima aspergillum).

ETHNOPHARMACOLOGICAL RELEVANCE: Dilong, the dried body of Pheretima aspergillum, is a classic traditional Chinese medicine that has been clinically us...

EJHF expert consensus statement on the diagnosis and management of hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is the most prevalent genetic cardiac disease and a leading cause of heart failure, arrhythmia, and sudden cardiac d...

Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study.

BACKGROUND: American Indian and Alaska Native communities experience disproportionately high suicide rates. While machine learning (ML) models leverag...