Exercise & Fitness

Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines.

BACKGROUND: Complication risks in children and adolescents with type 1 diabetes (T1D) can lead to serious health outcomes if not detected early. Despite the availability of clinical data, there remains a gap in interpretable tools that support risk stratification in this age group, particularly in alignment with local clinical guidelines. OBJECTIVE: The purpose of this study is to develop a clinic...

Artificial intelligence for left ventricular strain.

PURPOSE OF REVIEW: To review recent advances in artificial intelligence (AI) for left ventricular (LV) strain echocardiography, with emphasis on studies published during the preceding 18 months, and to assess current evidence for measurement performance, workflow integration, emerging AI-based approaches, disease-specific applications and barriers to widespread clinical adoption. RECENT FINDINGS: ...

Development and validation of an interpretable machine learning model for early risk prediction of acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) remains a leading cause of global morbidity and mortality, with early prediction critical for timely int...

Predictive modeling of injury risk based on body composition and physical fitness performance tests in professional football: A four-year study.

Preventing sports injuries in professional football enhances players' availability and performance. Machine learning, including artificial neural netw...

Temporal dynamics and associated factors of sleep quality in Chinese boarding high school students: a repeated cross-sectional study across four waves (2019-2024).

This study investigated temporal changes in sleep quality among Chinese boarding high school students across four phases (2019:pre-pandemic baseline, ...

An intelligent lung nodule classification model using 3D Trans-DenseUnet++-based lung nodule segmentation.

Lung nodule detectionis necessary for lung cancer treatment, which is crucial for treating the patients. However, the current dataset comprises only a...

Non-traditional metabolic indices predict incident circadian syndrome in middle-aged and older Chinese adults: a nationwide prospective cohort study and machine learning analysis.

BACKGROUND: Circadian syndrome (CircS) augments the conventional metabolic syndrome construct by adding disturbed sleep and depressive features. Wheth...

Measuring health professional students' willingness to use AI chatbots in learning: A fuzzy-set qualitative comparative analysis.

Artificial intelligence (AI)-driven chatbots showed great potential in medical education. However, users' willingness to adopt them varied considerabl...

How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.

The number of possible variants representing the landscape of a protein sequence of length N residues, made of the standard unmodified proteinogenic a...

Development and validation of a deep learning model for liver shear stiffness regression using abdominal multiparametric MRI across multiple sites and vendors.

OBJECTIVE: Liver stiffness measurement is important for assessing chronic liver disease (CLD). MR elastography (MRE) requires specialized hardware and...

The prediction of essential medicine demand using machine learning and traditional methods on EPSS Gondar hub 2018-2022 data.

INTRODUCTION: No prior research has utilized predictive models for forecasting essential medicine demand in Ethiopia. Therefore, this study aimed to i...

AI-driven multimodal simulation for psychiatric clinical education: conceptual framework and preliminary validation of the cognitive core.

BACKGROUND: The global shortage of psychiatrists limits learners' exposure to authentic patient encounters. Simulation can support scalable deliberate...

Thrombospondin-2 is a performant biomarker of at-risk MASH and advanced MASH fibrosis in a large multicentre European cohort.

BACKGROUND: Non-invasive biomarkers with biological rationale are needed for identifying patients with progressive metabolic dysfunction-associated st...

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...

Hybrid vision-IMU deep learning framework with graph convolutional networks and attention for personalized yoga posture identification.

Asanas, are very crucial for maintaining humans physical and mental health in an efficient manner. Digital training platforms, fitness monitors, and m...

Spatiotemporal skeleton based learning for automatic exercise quality assessment and real time posture correction.

BACKGROUND: Home-based fitness training requires automated systems for exercise quality assessment and real-time posture correction without profession...

Development and internal validation of an explainable machine-learning model to predict 3-year overall survival rate after radical cystectomy.

BACKGROUND: This study aimed to develop and internally validate an explainable machine-learning model using routinely available clinicopathologic and ...

Exploring the Bone-Vascular Axis: AI-Augmented Chest CT Analysis in COPD Highlights Association Between Vertebral Bone Density and Arterial Calcifications.

OBJECTIVES: Artificial intelligence (AI) tools enable automated assessment of vertebral bone mineral density (BMD), coronary artery calcification (CAC...

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...

Percutaneous endoscopic interlaminar discectomy in patients with lumbar disc herniation: risk factors and thresholds for adverse outcomes.

BACKGROUND: Percutaneous endoscopic interlaminar discectomy (PEID) is a common surgical technique for lumbar disc herniation (LDH), but the risk facto...