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Article

Assessing Mediterranean Diet Adherence with the Smartphone: The Medipiatto Project

1
ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland
2
Epidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, 8001 Zurich, Switzerland
3
Oviva S.A., 8852 Altendorf, Switzerland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Nutrients 2020, 12(12), 3763; https://doi.org/10.3390/nu12123763
Received: 22 October 2020 / Revised: 1 December 2020 / Accepted: 2 December 2020 / Published: 7 December 2020
(This article belongs to the Special Issue Nutrition Assessment Methodology: Current Update and Practice)
The Mediterranean diet (MD) is regarded as a healthy eating pattern with beneficial effects both for the decrease of the risk for non-communicable diseases and also for body weight reduction. In the current manuscript, we propose an automated smartphone application which monitors and evaluates the user’s adherence to MD using images of the food and drinks that they consume. We define a set of rules for automatic adherence estimation, which focuses on the main MD food groups. We use a combination of a convolutional neural network (CNN) and a graph convolutional network to detect the types of foods and quantities from the users’ food images and the defined set of rules to evaluate the adherence to MD. Our experiments show that our system outperforms a basic CNN in terms of recognizing food items and estimating quantity and yields comparable results as experienced dietitians when it comes to overall MD adherence estimation. As the system is novel, these results are promising; however, there is room for improvement of the accuracy by gathering and training with more data and certain refinements can be performed such as re-defining the set of rules to also be able to be used for sub-groups of MD (e.g., vegetarian type of MD). View Full-Text
Keywords: Mediterranean diet; Mediterranean diet score; Mediterranean diet adherence; artificial intelligence; machine learning; smartphone; computer vision Mediterranean diet; Mediterranean diet score; Mediterranean diet adherence; artificial intelligence; machine learning; smartphone; computer vision
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MDPI and ACS Style

Vasiloglou, M.F.; Lu, Y.; Stathopoulou, T.; Papathanail, I.; Faeh, D.; Ghosh, A.; Baumann, M.; Mougiakakou, S. Assessing Mediterranean Diet Adherence with the Smartphone: The Medipiatto Project. Nutrients 2020, 12, 3763. https://doi.org/10.3390/nu12123763

AMA Style

Vasiloglou MF, Lu Y, Stathopoulou T, Papathanail I, Faeh D, Ghosh A, Baumann M, Mougiakakou S. Assessing Mediterranean Diet Adherence with the Smartphone: The Medipiatto Project. Nutrients. 2020; 12(12):3763. https://doi.org/10.3390/nu12123763

Chicago/Turabian Style

Vasiloglou, Maria F., Ya Lu, Thomai Stathopoulou, Ioannis Papathanail, David Faeh, Arindam Ghosh, Manuel Baumann, and Stavroula Mougiakakou. 2020. "Assessing Mediterranean Diet Adherence with the Smartphone: The Medipiatto Project" Nutrients 12, no. 12: 3763. https://doi.org/10.3390/nu12123763

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