Mediterranean diet (MD) can play a major role in decreasing the risks of non-communicable diseases and preventing overweight and obesity. In order for a person to follow the MD and assess their adherence to it, proper dietary assessment methods are required. We have developed an Artificial Intelligence-powered system that recognizes the food and drink items from a single meal photo and estimates their respective serving size, and integrated it into a smartphone application that automatically calculates MD adherence score and outputs a weekly feedback report. We compared the MD adherence score of four users as calculated by the system versus an expert dietitian, and the mean difference was 3.5% and statistically not significant. Afterwards, we conducted a feasibility study with 24 participants, to evaluate the system's performance and to gather the users' and dietitians' feedback. The image recognition system achieved 61.8% mean Average Precision for the testing set and 57.3% for the feasibility study images (where the ground truth was taken as the participants' annotations). The feedback from the participants of the feasibility study was also very positive.
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