AIMC Topic: Diet Records
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Prediction of Fatty Acid Intake from Serum Fatty Acid Levels Using Machine Learning Technique in Women Living in Toyama Prefecture.
Journal of oleo science
Sep 20, 2024
Preventing lifestyle-related diseases requires understanding and managing the intake of total fats and specific types of fatty acids, especially trans fatty acids. There are several methods for measuring fat intake, each with its own strengths and li...
Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care.
Nutrients
Aug 5, 2024
For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps' features, quality, behaviour change potential, and comparati...
Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment.
Nutrients
Nov 30, 2023
Photo-based dietary assessment is becoming more feasible as artificial intelligence methods improve. However, advancement of these methods for dietary assessment in research settings has been hindered by the lack of an appropriate dataset against whi...
Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose.
Nutrients
Dec 13, 2019
The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrie...
Machine learning with sparse nutrition data to improve cardiovascular mortality risk prediction in the USA using nationally randomly sampled data.
BMJ open
Nov 28, 2019
OBJECTIVES: We aimed to test whether or not adding (1) nutrition predictor variables and/or (2) using machine learning models improves cardiovascular death prediction versus standard Cox models without nutrition predictor variables.
Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment.
Public health nutrition
Apr 6, 2018
OBJECTIVE: The present study tested the combination of an established and a validated food-choice research method (the 'fake food buffet') with a new food-matching technology to automate the data collection and analysis.
Automatic food detection in egocentric images using artificial intelligence technology.
Public health nutrition
Mar 26, 2018
OBJECTIVE: To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment.
Automatic diet monitoring: a review of computer vision and wearable sensor-based methods.
International journal of food sciences and nutrition
Jan 31, 2017
Food intake and eating habits have a significant impact on people's health. Widespread diseases, such as diabetes and obesity, are directly related to eating habits. Therefore, monitoring diet can be a substantial base for developing methods and serv...
INSIGHTS FROM MACHINE-LEARNED DIET SUCCESS PREDICTION.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2016
To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider "quantified self" movement and many opt-in...
Multiple hypotheses image segmentation and classification with application to dietary assessment.
IEEE journal of biomedical and health informatics
Jan 1, 2015
We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned...
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