Papathanail, Ioannis; Vasiloglou, Maria F; Stathopoulou, Thomai; Ghosh, Arindam; Baumann, Manuel; Faeh, David; Mougiakakou, Stavroula (2022). A feasibility study to assess Mediterranean Diet adherence using an AI-powered system. Scientific reports, 12(1), p. 17008. Springer Nature 10.1038/s41598-022-21421-y
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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.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Health and Nutrition |
Graduate School: |
Graduate School for Cellular and Biomedical Sciences (GCB) |
UniBE Contributor: |
Papathanail, Ioannis, Vasiloglou, Maria, Stathopoulou, Thomai, Mougiakakou, Stavroula |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health 600 Technology > 620 Engineering |
ISSN: |
2045-2322 |
Publisher: |
Springer Nature |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
13 Oct 2022 14:59 |
Last Modified: |
13 Jan 2023 22:37 |
Publisher DOI: |
10.1038/s41598-022-21421-y |
PubMed ID: |
36220998 |
BORIS DOI: |
10.48350/173708 |
URI: |
https://boris.unibe.ch/id/eprint/173708 |
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