Exercise & Fitness

Spinal cord FDG PET uptake in healthy adults: an AI-driven segmentation study.

PURPOSE: 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET)/Computerized Tomography (CT) is an important imaging modality in oncology, but its use in spinal cord assessment is limited due to a lack of baseline metabolic reference values. Regional spinal cord FDG uptake, especially in healthy populations, remains poorly defined, limiting accurate delineation between physiological and p...

AI-Powered Resting 12-Lead Electrocardiogram Algorithm for Predicting Low Peak Oxygen Consumption: Development and Validation Study.

BACKGROUND: Low peak oxygen consumption (V̇O2) is associated with higher cardiovascular and all-cause mortality, while improvements in peak V̇O2 reduce this risk. Although early detection allows timely intervention, practical screening tools remain lacking. As electrocardiograms (ECGs) reflect both cardiac and age-related changes, they may offer a viable screening approach. OBJECTIVE: This study a...

Benchmarking large language models for cell-free RNA diagnostic biomarker discovery.

Large language models can synthesize biomedical knowledge, parse vast amounts of data, and generate code, positioning them as promising tools for biom...

Deformable image registration for self-supervised cardiac phase detection in cardiac magnetic resonance images of patients with various diseases.

Cardiovascular magnetic resonance (CMR) is widely used to assess cardiac function, but individual cardiac cycles complicate automatic temporal compari...

Interpretable machine learning for endometriosis classification: a rule-based approach.

BACKGROUND: Endometriosis is a chronic gynecological disease characterized by the growth of endometrial-like tissue outside the uterus, leading to pel...

Predicting poor health-related quality of life among Chinese adolescents using explainable machine learning: the role of school adjustment, family context, and lifestyle factors.

BACKGROUND: Adolescents' health-related quality of life (HRQoL) is influenced by multiple domains, including school, family, and individual lifestyle....

An IBGWO optimized feature selection framework for sentiment analysis-based fraudulent website detection using MLRNN.

Detecting fraudulent websites is critical to ensuring network security and protecting users from online threats. Advanced machine learning algorithms ...

Development and external validation of a machine learning model for predicting post-CABG acute kidney injury: insights from MIMIC database and real-world cardiac surgery cohort.

OBJECTIVE: Acute kidney injury (AKI) is a severe complication following coronary artery bypass grafting(CABG) While machine learning models trained on...

A multimodal approach to equine thoracolumbar myofascial pain: integrating clinical examination, AI-based gait analysis, and the ridden horse pain ethogram.

BACKGROUND: Myofascial pain syndrome (MPS) is considered highly prevalent in horses, yet it is frequently underdiagnosed due to limited understanding ...

Interpretable Machine Learning to Predict Anxiety or Depression in Acute Ischemic Stroke after IV Thrombolysis: A Retrospective Cohort Study.

BACKGROUND: Acute Ischemic Stroke (AIS) represents a prevalent cerebrovascular condition characterized by significant levels of disability and mortali...

An explainable predictive machine learning model of oxaliplatin induced peripheral neuropathy based on clinical data: a retrospective single center.

BACKGROUND: Oxaliplatin (OXA) is widely used in the treatment of gastrointestinal malignancies such as colorectal cancer. However, oxaliplatin-induced...

A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models.

Aqueous solubility is an important property for assessing the druggability and ecotoxicological effects of molecules. Successful drug candidates shoul...

AI-based virtual patient training for clinical communication skills in dental undergraduates: A randomized controlled mixed methods study.

INTRODUCTION: Effective clinical communication is a core competency in dental education, yet traditional teaching methods often fail to provide authen...

Revisiting satisfaction with life: A hybrid study based on linear models and machine learning.

PURPOSE: Based on the Health Action Process Approach (HAPA) theory, this study examines how Health Literacy is associated with Satisfaction with Life ...

Integrating generative AI in EFL classrooms: A mixed-methods study on argumentative writing performance, writing enjoyment and motivation.

Writing in English as a Foreign Language (EFL) is widely recognized as one of the most challenging skills for language learners, and recent advances i...

Artificial intelligence as a clinical tool builder: Development and early implementation of an artificial intelligence-assisted hand therapy exercise application.

Physical activity level and its determinants in patients with cancer: a secondary analysis using a decision-tree approach.

PURPOSE: Low physical activity is associated with poorer outcomes in patients with cancer. Understanding the determinants of physical activity is esse...

Smartwatch-derived exercise metrics as predictors of early hypertension: a prospective observational study.

BACKGROUND: Early identification of individuals at risk for hypertension is essential for effective cardiovascular disease. Physiological and activity...

How work hours affect well-being: A target trial emulation.

Studies link longer work hours to multiple dimensions of well-being, but correlations do not show what would happen if hours changed. Target-trial emu...

CT ASSESSMENT OF BODY COMPOSITION AND LUNG EVOLUTION IN CYSTIC FIBROSIS AFTER ELEXACAFTOR-TEZACAFTOR-IVACAFTOR: A COHORT STUDY.

BACKGROUND: Elexacaftor-Tezacaftor-Ivacaftor (ETI) therapy markedly improves pulmonary function in people with cystic fibrosis (pwCF) but induces weig...