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. 2022 Apr 7;9:855223. doi: 10.3389/fnut.2022.855223

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Copyright © 2022 Schönenberger, Cossu, Prendin, Cappon, Wu, Fuchs, Mayer, Herzig, Facchinetti and Bally.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Technical flow of the SNAQ app. The app automatically segments pictures of meals into meal components and recognizes the food of the components. If the picture is taken with a depth-sensing camera of newer smartphones, it creates a depth map to estimate the volume of each meal component. From the volume and the food recognition, it can calculate the weight of each meal component. By using a food composition database, the macronutrient composition of the meal is calculated and displayed to the user with the corresponding CGM data. Screenshots kindly provided by SNAQ.