Automated Artificial Intelligence–Based Thai Food Dietary Assessment System: Development and Validation
Issued Date
2024-05-01
Resource Type
eISSN
24752991
Scopus ID
2-s2.0-85193009208
Journal Title
Current Developments in Nutrition
Volume
8
Issue
5
Rights Holder(s)
SCOPUS
Bibliographic Citation
Current Developments in Nutrition Vol.8 No.5 (2024)
Suggested Citation
Chotwanvirat P., Prachansuwan A., Sridonpai P., Kriengsinyos W. Automated Artificial Intelligence–Based Thai Food Dietary Assessment System: Development and Validation. Current Developments in Nutrition Vol.8 No.5 (2024). doi:10.1016/j.cdnut.2024.102154 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/98424
Title
Automated Artificial Intelligence–Based Thai Food Dietary Assessment System: Development and Validation
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Dietary assessment is a fundamental component of nutrition research and plays a pivotal role in managing chronic diseases. Traditional dietary assessment methods, particularly in the context of Thai cuisine, often require extensive training and may lead to estimation errors. Objectives: To address these challenges, Institute of Nutrition, Mahidol University (INMU) iFood, an innovative artificial intelligence–based Thai food dietary assessment system, allows for estimating the nutritive values of dishes from food images. Methods: INMU iFood leverages state-of-the-art technology and integrates a validated automated Thai food analysis system. Users can use 3 distinct input methods: food image recognition, manual input, and a convenient barcode scanner. This versatility simplifies the tracking of dietary intake while maximizing data quality at the individual level. The core improvement in INMU iFood can be attributed to 2 key factors, namely, the replacement of Yolov4-tiny with Yolov7 and the expansion of noncarbohydrate source foods in the training image data set. Results: This combination significantly enhances the system's ability to identify food items, especially in scenarios with closely packed food images, thus improving accuracy. Validation results showcase the superior performance of the INMU iFood integrated V7-based system over its predecessor, V4-based, with notable improvements in protein and fat estimation. Furthermore, INMU iFood addresses limitations by offering users the option to import additional food products via a barcode scanner, thus providing access to a vast database of nutritional information through Open Food Facts. This integration ensures users can track their dietary intake effectively, with expanded access to over 3000 food items added to or updated in the Open Food Facts database covering a wide variety of dietary choices. Conclusions: INMU iFood is a promising tool for researchers, health care professionals, and individuals seeking to monitor their dietary intake within the context of Thai cuisine and for ultimately promoting better health outcomes and facilitating nutrition-related research.