Diet & Nutrition

Detection of sarcopenic obesity and prediction of long-term survival in patients with gastric cancer using preoperative computed tomography and machine learning.

BACKGROUND: Previous studies evaluating the prognostic value of computed tomography (CT)-derived body composition data have included few patients. Thus, we assessed the prevalence and prognostic value of sarcopenic obesity in a large population of gastric cancer patients using preoperative CT, as nutritional status is a predictor of long-term survival after gastric cancer surgery.

A Review of Digital Innovations for Diet Monitoring and Precision Nutrition.

This article provides an up-to-date review of technological advances in 3 key areas related to diet monitoring and precision nutrition. First, we review developments in mobile applications, with a focus on food photography and artificial intelligence to facilitate the process of diet monitoring. Second, we review advances in 2 types of wearable and handheld sensors that can potentially be used to ...

The effect of consuming different proportions of hummer fish on biochemical and histopathological changes of hyperglycemic rats.

Hammour fish (grouper fish) are known to be of great nutritional value for human consumption, as their protein has a high biological value and contain...

Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing.

Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, whi...

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis.

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes i...

Towards personalized nutritional treatment for malnutrition using machine learning-based screening tools.

Early identification of patients at risk of malnutrition or who are malnourished is crucial in order to start a timely and adequate nutritional therap...

Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis.

: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-...

Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.

The epidemic increase in the incidence of Human Papilloma Virus (HPV) related Oropharyngeal Squamous Cell Carcinomas (OPSCCs) in several countries wor...

Prediction Algorithm of Young Students' Physical Health Risk Factors Based on Deep Learning.

Young people's physical and mental health is the foundation of society's overall development and the key to improving people's health quality. Middle ...

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms.

In this study, we developed machine learning-based prediction models for early childhood caries and compared their performances with the traditional r...

Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study.

Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as pos...

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder.

Assessing the quality of food and spices is particularly important in ensuring proper human nutrition. The use of computer vision method as a non-dest...

Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study.

In this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for di...

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies.

BACKGROUND: There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and ...

Editorial: The National COVID Cohort Collaborative Consortium Combines Population Data with Machine Learning to Evaluate and Predict Risk Factors for the Severity of COVID-19.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) commonly presents with pne...

Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.

This investigation aimed to develop a method to predict the total soluble solids (TSS), titratable acidity, TSS/titratable acidity, vitamin C, anthocy...

Applicability of machine learning techniques in food intake assessment: A systematic review.

The evaluation of food intake is important in scientific research and clinical practice to understand the relationship between diet and health conditi...

Data-driven identification of complex disease phenotypes.

Disease interaction in multimorbid patients is relevant to treatment and prognosis, yet poorly understood. In the present work, we combine approaches ...

Virtual Reality-Based Framework to Simulate Control Algorithms for Robotic Assistance and Rehabilitation Tasks through a Standing Wheelchair.

The implementation of control algorithms oriented to robotic assistance and rehabilitation tasks for people with motor disabilities has been of increa...

Transmol: repurposing a language model for molecular generation.

Recent advances in convolutional neural networks have inspired the application of deep learning to other disciplines. Even though image processing and...