Exercise & Fitness

Developing a machine learning-based predictive model for levothyroxine dosage estimation in hypothyroid patients: a retrospective study.

Hypothyroidism, a common endocrine disorder, has a high incidence in women and increases with age. Levothyroxine (LT4) is the standard therapy; however, achieving clinical and biochemical euthyroidism is challenging. Therefore, developing an accurate model for predicting LT4 dosage is crucial. This retrospective study aimed to identify factors affecting the daily dose of LT4 and develop a model to...

Harnessing generative AI in exercise and sports science education: enhancing real-world learning and overcoming traditional barriers in data analysis.

Generative AI (GenAI) offers transformative potential for exercise and sports science education, addressing traditional data analysis and visualization barriers while promoting real-world learning. This Perspectives article explores how integrating GenAI into exercise and sports science degrees can enhance students' ability to interpret complex physiological data, improve their analytical skills, ...

Advancing Sports Cardiology: Integrating Artificial Intelligence with Wearable Devices for Cardiovascular Health Management.

Sports cardiology focuses on athletes' cardiovascular health, yet sudden cardiac death remains a significant concern despite preventative measures. Pr...

Engineering highly active nuclease enzymes with machine learning and high-throughput screening.

Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged f...

Identifying individuals at risk for weight gain using machine learning in electronic medical records from the United States.

AIMS: Numerous risk factors for the development of obesity have been identified, yet the aetiology is not well understood. Traditional statistical met...

Leveraging Generative Artificial Intelligence to Improve Motivation and Retrieval in Higher Education Learners.

Generative artificial intelligence (GenAI) presents novel approaches to enhance motivation, curriculum structure and development, and learning and ret...

Early prediction of cardiovascular events following treatments in female breast cancer patients: Application of real-world data and artificial intelligence.

• Application of real-world data and artificial intelligence in detecting cardiotoxicity following cancer treatment. • Clinical features have been use...

Multitask Deep Learning Models of Combined Industrial Absorption, Distribution, Metabolism, and Excretion Datasets to Improve Generalization.

The optimization of absorption, distribution, metabolism, and excretion (ADME) profiles of compounds is critical to the drug discovery process. As suc...

Early prediction of postpartum dyslipidemia in gestational diabetes using machine learning models.

This study addresses a gap in research on predictive models for postpartum dyslipidemia in women with gestational diabetes mellitus (GDM). The goal wa...

Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images.

In recent times, severe acute malnutrition (SAM) in India is considered a serious issue as per UNICEF 2022 records. In that record, 35.5% of children ...

Fitness for Purpose of Text-to-Image Generative Artificial Intelligence Image Creation in Medical Imaging.

The recent emergence of text-to-image generative artificial intelligence (AI) diffusion models such as DALL-E, Firefly, Stable Diffusion, and Midjourn...

Comparison of predictive models for knee pain and analysis of individual and physical activity variables using interpretable machine learning.

BACKGROUND: Knee pain is associated with not only individual factors such as age and obesity but also physical activity factors such as occupational a...

Interpretation of cardiopulmonary exercise test by GPT - promising tool as a first step to identify normal results.

BACKGROUND: Cardiopulmonary exercise testing (CPET) is used in the evaluation of unexplained dyspnea. However, its interpretation requires expertise t...

Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant.

Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alt...

The predictive role of sedentary behavior and physical activity on adolescent depressive symptoms: A machine learning approach.

OBJECTIVE: This study aims to investigate the predictive value of sedentary behavior and physical activity in adolescent depressive symptoms.

Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association.

Artificial intelligence is poised to transform cardio-oncology by enabling personalized care for patients with cancer, who are at a heightened risk of...

Cognitive performance classification of older patients using machine learning and electronic medical records.

Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide. Developing efficient ...

Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.

PURPOSE OF REVIEW: This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problem...

An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm.

Diet, stress, genetics, and a sedentary lifestyle may all contribute to heart disease rates. Although recent studies propose comprehensive automated d...

Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching.

Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial ...