Diet & Nutrition

Artificial intelligence and chest CT: a new frontier for opportunistic bone mineral density assessment.

Enlarged Perivascular Spaces and Modifiable Vascular Risk Factors: Cross-Sectional and Longitudinal Analysis in the UK Biobank Cohort.

OBJECTIVE: To examine cross-sectional and longitudinal associations between vascular risk factors, APOE genotype, and perivascular spaces (PVS), with attention to sex- and region-specific patterns in older adults. DESIGN: Population-based observational study using automated PVS quantification and multivariable regression models. SETTING: UK Biobank, a large prospective cohort study of community-dw...

Integrating education-based interventions and machine learning for stunting prevention: A case study in East Lombok, Indonesia.

One of the regions in Indonesia that has the highest prevalence of stunting cases is West Nusa Tenggara, with a percentage of cases almost reaching 12...

Can environmental signals influence dietary Behaviours?The impact of governmental green development attention on dietary diversity among older adults.

Against the backdrop of simultaneous national agendas for green transition and healthy ageing, the nutritional behaviour of older adults is being resh...

Development of advanced lung cancer inflammation index-based machine learning models for predicting stroke and mortality: A comparative and interpretable study.

BACKGROUND: The Advanced Lung Cancer Inflammation Index (ALI) is a novel composite index that enables a more holistic evaluation of inflammation and n...

Gut microbiome profiles and associated functional pathways are linked to Mediterranean diet adherence and blood glucose control in adults with type 1 diabetes mellitus.

BACKGROUND AND AIMS: The Mediterranean diet (MD) has been associated with better glycaemic control in children with type 1 diabetes mellitus (T1DM) an...

Development of an explainable machine learning model to reproduce and interpret expert pharmacological decisions in osteoporosis treatment.

The management of osteoporosis in real-world clinical practice is highly heterogeneous, reflecting the complexity and variability inherent in therapeu...

Building and validating a machine learning model to predict coronary heart disease risk based on non-invasive indicators.

BACKGROUND: Coronary heart disease (CHD) remains a leading global cause of death. Early identification of high-risk individuals and timely interventio...

Using machine learning to predict clinical remission with exclusive enteral nutrition in pediatric Crohn disease.

OBJECTIVES: Exclusive enteral nutrition (EEN) is a first-line treatment for induction of remission in luminal pediatric Crohn disease (pCD). However, ...

Global analysis of mineral aerosol and industrial effects on lake water quality using spectral indices and machine learning.

The complex physical and chemical interactions between desert mineral aerosol, industrial pollutants, and lake water during drought periods pose signi...

Artificial intelligence-guided nutritional therapy in the ICU.

PURPOSE OF REVIEW: Critical care nutrition remains a high-stakes and error-prone domain, particularly given the complex metabolic demands and heteroge...

Artificial intelligence in clinical nutrition. A narrative review.

The rapid integration of Artificial Intelligence (AI) into healthcare, particularly clinical nutrition, holds transformative potential for enhanced di...

Machine Learning-Based Transcriptomic Diagnosis of Periodontitis.

BACKGROUND: Periodontitis, a prevalent chronic inflammatory disease, remains a global health challenge with conventional diagnostic methods hindered b...

Cancer as a main contributor to multimorbidity in the UK Biobank: analysis of risk factors using conventional statistical modelling and machine learning.

Cancer frequently co-occurs with other chronic conditions, contributing substantially to disease burden. Multimorbidity is an increasing public health...

Integrating multi-omics data for personalized nutrition using knowledge graphs and graph neural networks: A comprehensive review.

This comprehensive review focuses on the use of knowledge graphs and Graph Neural Networks (GNNs) to integrate multi-omics data in the field of person...

Optimal adherence thresholds for oral anticoagulants in patients with atrial fibrillation using machine learning and population administrative data.

BACKGROUND: Adherence to oral anticoagulants (OACs) for atrial fibrillation (AF) stroke prevention is traditionally defined as taking 80% of doses as ...

Comparing ChatGPT with healthcare provider responses to home parenteral nutrition questions.

BACKGROUND: Patients receiving home parenteral nutrition (HPN) face complex challenges and increasingly seek support online, including generative arti...

Artificial intelligence (AI) in nutrition: A case-based comparison of generative AI models.

BACKGROUND: Clinical nutrition (CN) is becoming increasingly complex because of the rising prevalence of chronic illness, cancer, and malnutrition-rel...

Combination of artificial intelligence and chest computed tomography to assess bone mineral density.

OBJECTIVES: To evaluate the diagnostic accuracy of artificial intelligence-assisted opportunistic chest CT for osteoporosis/osteopenia screening in a ...

Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans.

OBJECTIVES: To develop and do multicenter validation on an algorithm that screens for osteoporosis from abdominal CTs. METHODS: This is a diagnostic a...