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Year 4 Research Project
Prediction of Health Effects from Food Photos

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Description

AI has the potential to enable consumers to learn the health effects of their food far beyond what is available on a Nutrition Facts Label. The long-term goal of this project is to empower users to know what they eat, and what it does to their health. To achieve that, we will (a) develop a “food photo to ingredients” module and a “ingredients to health outcome” module, each of which will be needed in most computational systems in nutrition. This project will be developed in close collaboration with the Dietary Assistant project, and they will both integrate in a cellphone application that will be created this year. The app will be deployed in a clinical trial in the following year to measure the impact of AI-enabled consumer education on consumer dietary quality.

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Team

Portrait of Danielle Lemay

Danielle Lemay

Principal Investigator

Portrait of Hamed Pirsiavash

Hamed Pirsiavash

Co Principal Investigator

Portrait of Jennifer Smilowitz

Jennifer Smilowitz

Co Principal Investigator

Portrait of Ilias Tagkopoulos

Ilias Tagkopoulos

Co Principal Investigator

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Publications

A thumbnail of the journal or conference cover of Dietary Intake of Monosaccharides from Foods is Associated with Characteristics of the Gut Microbiota and Gastrointestinal Inflammation in Healthy US Adults
Journal Article ⏐ J. Nutr. 2023

Dietary Intake of Monosaccharides From Foods Is Associated with Characteristics of the Gut Microbiota and Gastrointestinal Inflammation in Healthy US Adults

Larke, Jules A,Nikita P Bacalzo,Juan J Castillo,Garret Couture,Ye Chen,Zhengyao Xue,Zeynep Alkan,Mary E Kable,Carlito B Lebrilla,Charles B Stephensen,and Danielle G Lemay
DOI: 10.1016/j.tjnut.2022.12.008
A thumbnail of the journal or conference cover of Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment
Journal Article ⏐ Nutrients 2023

Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment

DOI: 10.3390/nu15234972