Primary Care

Digital and Blended Lifestyle Interventions for Preschool-Aged Children and Families With a Low Socioeconomic Position and the General Population: Scoping Review.

BACKGROUND: Many unhealthy habits develop in early childhood and can lead to long-term health risks, which disproportionately affect children with low socioeconomic position (SEP). Digital and blended lifestyle interventions can promote healthier lifestyles, yet families with lower SEP remain underrepresented and face unique barriers to healthy behaviors and intervention access. As a result, it re...

Exploratory eye movement characteristics to aid screening of schizophrenia and bipolar disorder: A cross-sectional outpatient study.

Current psychiatric diagnoses lack objective criteria, and this study aims to evaluate EEM as a potential tool for improving diagnostic objectivity across psychiatric disorders. The Exploratory Eye Movement (EEM) paradigm was used to analyze eye-movement data from patients with schizophrenia or bipolar disorder and healthy controls. Key metrics included Number of Eye Fixations (NEF), Total Eye Sca...

Development, internal testing, and external validation of an interpretable machine-learning model for predicting imaging-confirmed in-hospital postoperative deep vein thrombosis in older patients with patellar fractures.

BACKGROUND: Older patients with patellar fractures may be at increased risk of postoperative deep vein thrombosis (DVT) because of trauma, perioperati...

Non-invasive periodontal screening using self-reported-oral-health (SROH) questionnaire and salivary biomarkers: development and validation of machine learning models.

BACKGROUND: Accurate and accessible screening tools for periodontitis are essential for early detection and disease prevention. This study evaluated a...

Deep Learning-Derived Retinal Age Detects Cognitive Impairment.

PURPOSE: To investigate the association between deep learning-derived retinal age and cognitive function and to evaluate whether retinal age outperfor...

TASC: a time-aware sequence clustering framework with uncertainty quantification for electronic health record trajectories.

Longitudinal electronic health record (EHR) trajectories are highly heterogeneous, sparse, and irregular, making unsupervised temporal pattern discove...

An explainable machine learning method for identifying key atherogenic lipid biomarkers in abdominal obesity among the Southern Chinese population.

BACKGROUND: Abdominal obesity (AO) significantly contributes to cardiometabolic diseases and poses an increasing public health challenge. Composite li...

Multi-omics analysis identifies CD48 and ITIH5 as potential diagnostic biomarkers and therapeutic targets for metabolic disorders associated with obesity.

BACKGROUND: Obesity-driven metabolic disorders involve pathological remodeling of white adipose tissue (WAT), wherein fibroblasts coordinate fibrosis ...

Machine learning, bioinformatics analysis and chemical screening streamline target validation by identifying RNA helicase as a druggable essential protein in tobacco mosaic virus.

INTRODUCTION: Plant viruses inflict annual economic losses exceeding $30 billion and pose a significant threat to global food security. Discovering re...

Smartphone-Based Proactive Self-Screening for Ocular Surface Malignancies: A Nonrandomized Clinical Trial.

IMPORTANCE: Ocular surface malignancies pose risks to vision and survival yet are frequently misdiagnosed as benign lesions because of their subtle pr...

Advancing Gastrointestinal Cancer Risk Prediction With Patient-Centered Machine Learning: Machine Learning Modeling Study.

BACKGROUND: Gastrointestinal (GI) cancers are a significant health concern in South Korea. Recently, machine learning (ML) models have emerged as powe...

Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study.

BACKGROUND: Underdiagnosis and undercoding are common across mental health conditions, particularly suicide and self-harm. This leaves health care dat...

Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.

BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established c...

[State of the art and innovations in breast cancer prevention and screening strategies].

Breast cancer is the most common cancer among women, and its incidence is increasing, particularly in France, due to changes in lifestyle and exposure...

Wearable, broadband auscultation patch with cantilever pressure transducer for remote healthcare monitoring.

Flexible and wearable devices employing acoustic sensors have emerged as promising alternatives for continuous monitoring of physiological mechano-aco...

Beyond composite scores in chronotype assessment: item-level predictive patterns in the Morningness-Eveningness Questionnaire.

Chronotype represents individual differences in circadian preferences that influence sleep-wake patterns, cognitive performance, and clinical outcomes...

Label-free interferometry platform for drug response profiling of bioprinted tumor organoids at single-organoid resolution.

Organoids have become mainstay tools for drug discovery and personalized medicine. High-throughput imaging readouts for drug screening of tumor organo...

Physics-informed deep learning enables reliable and scalable organoid quantification for drug screening via OCT.

Patient-derived organoids (PDOs) hold transformative potential for personalized medicine by recapitulating patient-specific drug responses. While Opti...

Body composition phenotyping of obesity in children aged 6-18 years: multi-strategy clustering and interpretable machine learning.

BACKGROUND: Body composition heterogeneity in childhood obesity is not fully captured by BMI, motivating operational phenotyping using non-invasive me...

Artificial intelligence for right ventricular assessment: current evidence and future directions.

PURPOSE OF REVIEW: Right ventricular size and function are vital to risk stratification in pulmonary hypertension, valvular disease, and congenital he...