Accurate dietary assessment is crucial for both the prevention and treatment of nutrition-related diseases. Since mobile-based dietary assessment solutions are promising, we sought to examine the acceptability of “Nutrition and Diet” (ND) apps by Healthcare Professionals (HCP), explore their preferences on apps’ features and identify predictors of acceptance. A 23 question survey was developed by an interdisciplinary team and pilot-tested. The survey was completed by 1001 HCP from 73 countries and 6 continents. The HCP (dietitians: 833, doctors: 75, nurses: 62, other: 31/females: 847, males: 150, neither: 4) had a mean age (SD) of 34.4 (10.2) years and mean job experience in years (SD): 7.7 (8.2). There were 45.5% who have recommended ND apps to their clients/patients. Of those who have not yet recommended an app, 22.5% do not know of their existence. Important criteria for selecting an app were ease of use (87.1%), apps being free of charge (72.6%) and validated (69%). Significant barriers were the use of inaccurate food composition database (52%), lack of local food composition database support (48.2%) and tech-savviness (43.3%). Although the adoption of smartphones is growing and mobile health research is advancing, there is room for improvement in the recommendation of ND apps by HCP.

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Accurate estimation of nutritional information may lead to healthier diets and better clinical outcomes. We propose a dietary assessment system based on artificial intelligence (AI), named goFOODTM. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOODTM requires an input of two meal images or a short video. For conventional single-camera smartphones, the images must be captured from two different viewing angles; smartphones equipped with two rear cameras require only a single press of the shutter button. The deep neural networks are used to process the two images and implements food detection, segmentation and recognition, while a 3D reconstruction algorithm estimates the food’s volume. Each meal’s calorie and macronutrient content is calculated from the food category, volume and the nutrient database. goFOODTM supports 319 fine-grained food categories, and has been validated on two multimedia databases that contain non-standardized and fast food meals. The experimental results demonstrate that goFOODTM performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOODTM provides a simple and efficient solution to the end-user for dietary assessment.

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).