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Grant Details

Grant Number: 1R21CA250024-01A1 Interpret this number
Primary Investigator: Hennessy, Erin
Organization: Tufts University Boston
Project Title: AC/DC: Artificial Intelligence and Computer Visioning to Assess Dietary Composition
Fiscal Year: 2020


Abstract

ABSTRACT Dietary intake is a complex human behavior that drives disease risk and corresponding economic and healthcare burdens worldwide. Poor diet is the leading cause of death in the US and a known driver of obesity – a global epidemic. A major contributor to poor diet is food eaten away from home, such as restaurant foods. Research has shown that tracking one’s weight and dietary intake significantly improve success toward weight loss and maintenance goals; however, this type of tracking is burdensome, prone to error, and difficult to estimate for restaurant foods. Accurate approaches and tools to evaluate food and nutrient intake are essential in monitoring the nutritional status of individuals. There is a critical need for real-time data capture that minimizes burden and reduces error. While progress has been made, there is no tool available that accurately and automatically estimates foods left unconsumed in a meal. Two major limitations of existing systems is the reliance of a fiducial marker for food detection and volume estimation, and reliance on humans – either the respondent or a trained researcher – to estimate the portion of food leftover. This application leverages novel technology to remove those limitations. The long-term research goal is to utilize digital imaging (DI), artificial intelligence (AI) and computer vision (CV) techniques to develop a novel hybrid methodology for rapid, accurate measurement of dietary intake. To attain this goal, our objective in this R21 application is to refine and test a system architecture that (a) uses digital images to record dietary intake in real-time and (b) uses AI and CV techniques to identify food/beverage items and determine amounts leftover. We plan to build on our current prototype in which digital food images are captured before and after the meal, analyzed to detect the food items, a three-dimensional (3-D) virtual model constructed, and volume remaining after the meal estimated, which will be used to calculate the amount leftover based on the initial volume. Volume consumed will be converted to weight and linked to public-use nutrition information. These calorie estimates will be compared against calories those from (a) DIs coded by trained research staff and (b) weighed plate waste methodology. Our expectation is to develop a valid system architecture for rapidly estimating dietary intake. The outcome of this proposal is expected to have a significant positive impact, enabling nutrition and health researchers to collect high-quality food consumption data in real world settings, increasing knowledge of dietary patterns and improving capacity to assess dietary interventions. This work will lead to an R01 application that will expand food types and meal settings and test the utility of our system among consumers.



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