چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده | نفر چندم مقاله |
|---|---|
| مریم رف رف | اول |
| رقیه مولانی | دوم |
| عنوان | متن |
|---|---|
| خلاصه مقاله | Background: Multiple applications of artificial intelligence (AI) in medical sciences are grow- ing rapidly in the recent years. AI technologies became complementary to the food science and nutrition research areas in the late 2010s. AI provides new opportunities for research on nutrients and medical sensing technology. The application of AI in the nutritional epidemiology field, in particular, dietary assessment, has been reported in several recent studies, however, any study didn’t summarize comprehensively these findings. This systematic review aimed to provide an overview of the main and latest applications of AI in dietary assessment research and identify gaps to address to potentialize this emerging field. Methods: This study were conducted with considering the PRISMA guidelines. The literature search was conducted in PubMed, Scopus, and Google Scholar without date restriction up to Feb- ruary 2023. The search strategy was expanded using a combination of MeSH terms and the fol- lowing keywords: “artificial intelligence” AND “dietary assessment” OR “nutrient”. Moreover, a manual search of the references list of eligible studies and the Google was done to minimize the risk of missing relevant papers. All original articles written in English that evaluated the applica- tion of AI for dietary assessment of participants were eligible for the present review. Results: After screening the title, abstract, and full text of obtained articles by two independent authors, finally, 9 studies were included in the current review. The included studies were published from 2008 to 2022. The used predominant algorithms in included studies were machine learning and deep learning to estimate food portion size and estimate the calorie and macronutrient content of a meal. Moreover, the included studies suggested the use of smartphone and image-based and web-based dietary assessment apps in nutritional epidemiology. Conclusions: AI-based approaches including mobile apps and image recognition can improve dietary assessment by addressing random errors in self-reported measurements of dietary intakes. Further research is needed to identify and develop new AI-based approaches for dietary assess- ment in nutrition research. Furthermore, well-designed studies with large sample sizes are re- quired to confirm the beneficial health outcomes of AI use among different age groups of the population. Keywords: Artificial intelligence, Dietary assessment, Nutrition, Nutrient |
| کلمات کلیدی | Keywords: Artificial intelligence, Dietary assessment, Nutrition, Nutrient |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| book-F-01.pdf | 1402/03/24 | 3540441 | دانلود |
| ghavahi.hosh.pdf | 1402/06/18 | 95482 | دانلود |
