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Improving fine-grained food classification using deep residual learning and selective state space models.

BACKGROUND: Food classification is the foundation for developing food vision tasks and plays a key r...

Associations between urinary and blood heavy metal exposure and heart failure in elderly adults: Insights from an interpretable machine learning model based on NHANES (2003-2020).

BACKGROUND: The relationship between heavy metal exposure and heart failure is complex and poorly un...

Unraveling MASLD: The Role of Gut Microbiota, Dietary Modulation, and AI-Driven Lifestyle Interventions.

Gut microbiota has a crucial role in the pathophysiology of metabolic-associated steatotic liver dis...

ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Application of modeling innovations to support satellite remote sensing for sustainable grazing cattle management.

Integrating modeling innovations and satellite remote sensing technology offers a transformative app...

ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Harnessing Real-Time Data and Digital Twins for Precision Livestock Farming.

The increasing population and urbanization have intensified livestock production, raising concerns a...

Enriching patient populations in ICU trials: reducing heterogeneity through machine learning.

PURPOSE OF REVIEW: Despite the pivotal role of randomized controlled trials (RCTs) in critical care ...

Natural Language Processing and Machine Learning Techniques for Analyzing Conversations About Nutritional Yeasts in the United States and France: Retrospective Social Media Listening Study.

BACKGROUND: Nutritional yeast, an inactive form of Saccharomyces cerevisiae, has recently become inc...

Factors influencing short-term and long-term survival rates in stroke patients receiving enteral nutrition: a machine learning approach using MIMIC-IV database.

PURPOSE: This study aims to evaluate the survival and mortality rates of stroke patients after recei...

An artificial intelligence malnutrition screening tool based on electronic medical records.

BACKGROUND & AIMS: Nutrition screening is a fundamental step to ensure appropriate intervention in p...

Modeling the number of new cases of childhood type 1 diabetes using Poisson regression and machine learning methods; a case study in Saudi Arabia.

Diabetes mellitus stands out as one of the most prevalent chronic conditions affecting pediatric pop...

Constructing machine learning-based risk prediction model for osteoarthritis in population aged 45 and above: NHANES 2011-2018.

Osteoarthritis is a widespread chronic joint disease, becoming increasingly prevalent, particularly ...

Dynamic simulation of knee joint mechanics: individualized multi-moment finite element modelling of patellar tendon stress during landing.

Patellar tendinopathy is prevalent in sports requiring high jumping demands, and understanding the i...

SmartBerry for AI-based growth stage classification and precision nutrition management in strawberry cultivation.

Agriculture is vital for human sustenance and economic stability, with increasing global food demand...

Artificial intelligence real-time automated recognition of the gastric antrum cross-sectional area and motility rhythm via bedside ultrasound: a pilot study.

The cross-sectional area (CSA) of the gastric antrum and its motility rhythm reflects the gastrointe...

Rigorous validation of machine learning in laboratory medicine: guidance toward quality improvement.

The application of artificial intelligence (AI) in laboratory medicine will revolutionize predictive...

Deep learning for microbiome-informed precision nutrition.

Machine learning model for age related macular degeneration based on pesticides: the National Health and Nutrition Examination Survey 2007-2008.

Age-related macular degeneration (AMD) is the most common cause of irreversible deterioration of vis...

Physiological and morphological impact of physical activity and nutritional interventions to offset disuse-induced skeletal muscle atrophy.

Skeletal muscle tissue acts as a functional unit for physical movements, energy metabolism, thermoge...

Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.

Early and accurate identification of patients at high risk of metabolic dysfunction-associated steat...

The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007-2018.

The association between depression severity and cardiovascular health (CVH) represented by Life's E...

Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting hear...