Diet & Nutrition

Evaluating Large Multimodal Models for Nutrition Analysis: A Benchmark Enriched with Contextual Metadata

Large Multimodal Models (LMMs) are increasingly applied to meal images for nutrition analysis. However, existing work primarily evaluates proprietary models, such as GPT-4. This leaves the broad range of LLMs underexplored. Additionally, the influence of integrating contextual metadata and its interaction with various reasoning modifiers remains largely uncharted. This work investigates how inte...

Adopting a human developmental visual diet yields robust, shape-based AI vision

Despite years of research and the dramatic scaling of artificial intelligence (AI) systems, a striking misalignment between artificial and human vision persists. Contrary to humans, AI heavily relies on texture-features rather than shape information, lacks robustness to image distortions, remains highly vulnerable to adversarial attacks, and struggles to recognise simple abstract shapes within c...

Predicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms.

BACKGROUND: Machine learning systems learn from historical data to forecast future outcomes. In the context of livestock farming, machine learning can...

Innovations in Quail Welfare: Integrating Environmental Enrichment, Nutrition and Genetic Advances for Improved Health and Productivity.

The demand for ethical and sustainable poultry production is driving up the importance of quail welfare. Because quail meat and eggs are in high deman...

Automatic adult age estimation using bone mineral density of proximal femur via deep learning.

Accurate adult age estimation (AAE) is critical for forensic and anthropological applications, yet traditional methods relying on bone mineral density...

Laryngopharyngeal Reflux Prevalence and Predictors Among Doctors.

Laryngopharyngeal reflux (LPR) refers to the retrograde flow of stomach contents into the larynx due to an abnormality involving the upper oesophageal...

Development and validation of an interpretable machine learning model for predicting hyperuricemia risk: Based on environmental chemical exposure.

Hyperuricemia is a global health concern, with environmental chemicals as risk factors. This study used data of multiple environmental chemical exposu...

Development and validation of explainable machine learning models for female hip osteoporosis using electronic health records.

BACKGROUND: Hip fractures are associated with reduced mobility, and higher morbidity, mortality, and healthcare costs. Approximately 90% of hip fractu...

Associations of dietary patterns with serum 25(OH) vitamin D and serum anemia related biomarkers among expectant mothers: A machine learning based approach.

BACKGROUND: Machine learning algorithms (MLA) gained prominence in nutritional epidemiology for analyzing dietary associations and uncovering intricat...

Comparing logistic regression and machine learning for obesity risk prediction: A systematic review and meta-analysis.

BACKGROUND: Logistic regression (LR) has traditionally been the standard method used for predicting binary health outcomes; however, machine learning ...

CoMMiT: Co-informed inference of microbiome-metabolome interactions via transfer learning

Recent multi-omic microbiome studies enable integrative analysis of microbes and metabolites, uncovering their associations with various host condit...

A general deep learning model for predicting and classifying pea protein content via visible and near-infrared spectroscopy.

Rapid and accurate detection of pea protein content is crucial for breeding and ensuring food quality. This study introduces the PeaNet model, which e...

Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration

Osteoporosis, characterized by reduced bone mineral density (BMD) and compromised bone microstructure, increases fracture risk in aging populations....

Machine learning: An effective tool for monitoring and ensuring food safety, quality, and nutrition.

The domains of food safety, quality, and nutrition are inundated with complex datasets. Machine learning (ML) has emerged as a powerful tool in food s...

PulseRide: A Robotic Wheelchair for Personalized Exertion Control with Human-in-the-Loop Reinforcement Learning

Maintaining an active lifestyle is vital for quality of life, yet challenging for wheelchair users. For instance, powered wheelchairs face increasin...

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.

Mendelian randomization (MR) enables the estimation of causal effects while controlling for unmeasured confounding factors. However, traditional MR's ...

VolTex: Food Volume Estimation using Text-Guided Segmentation and Neural Surface Reconstruction

Accurate food volume estimation is crucial for dietary monitoring, medical nutrition management, and food intake analysis. Existing 3D Food Volume e...

Predicting Stress, Anxiety, and Depression in Adult Men Based on Nutritional and Lifestyle Variables: A Comparative Analysis of Machine Learning Methods.

Mental health disorders like depression, anxiety, and stress (DAS) are rising globally. Understanding how diet and lifestyle influence these condition...

Multidimensional computational strategies enhance the thermostability of alpha-galactosidase.

Alpha-Galactosidase has significant industrial application value in food processing, animal nutrition and medical applications. Microbial-derived α-ga...

Ripen banana dataset: A comprehensive resource for carbide detection and ripening stage analysis to enhance food quality and agricultural efficiency.

We introduce the "Ripen Banana" dataset, a newly developed collection featuring two distinct classes of ripen banana images: carbide and non-carbide. ...