Flag Counter
AKILLI SÄ°STEMLER VE UYGULAMALARI DERGÄ°SÄ°
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Artificial Intelligence Applications in Nutrition and Dietetics

Beslenme ve Diyetetikte Yapay Zeka Uygulamaları

How to cite: Ãœlker Ä, Ayyıldız F. Artificial intelligence applications in nutrition and dietetics. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2021; 4(2): 125-127. DOI: 10.54856/jiswa.202112175

Full Text: PDF, in English.

Total number of downloads: 691

Title: Artificial Intelligence Applications in Nutrition and Dietetics

Abstract: Artificial intelligence (AI) is a branch of computer science whose purpose is to imitate thought processes, learning abilities, and knowledge management. The increasing number of applications in experimental and clinical medicine is striking. An artificial intelligence application in the field of nutrition and dietetics is a fairly new and important field. Different apps related to nutrition are offered to the use of individuals. The importance of individual nutrition has also triggered the increase in artificial intelligence apps. It is thought that different apps such as food preferences and dietary intake can play an important role in health promotion. Researchers may have some difficulties such as remembering the frequency or amount of intake in assessment of dietary intake. Some applications used in the assessment of food consumption contribute to overcoming these difficulties. Besides, these apps facilitate the work of researchers and provide more reliable results than traditional methods. The apps to be used in the field of nutrition and dietetics should be developed by considering the disadvantages. It is thought that artificial intelligence applications will contribute to both the improvement of health and the assessment and monitoring of nutritional status.

Keywords: Nutrition and dietetics; artificial intelligence; health


Başlık: Beslenme ve Diyetetikte Yapay Zeka Uygulamaları

Özet: Yapay zeka (AI), amacı düşünce süreçlerini, öğrenme yeteneklerini ve bilgi yönetimini taklit etmek olan bir bilgisayar bilimi dalıdır. Deneysel ve klinik tıpta giderek artan uygulama sayısı dikkat çekicidir. Beslenme ve diyetetik alanında yapay zeka uygulamaları oldukça yeni ve önemli bir alandır. Bireylerin kullanımına beslenme ile ilgili farklı uygulamalar sunulmaktadır. Bireysel beslenmenin öneminin anlaşılması, yapay zeka uygulamalarında artışa destek olmaktadır. Besin tercihleri ve diyetle alımın değerlendirilmesi gibi farklı uygulamaların sağlığın geliştirilmesinde önemli bir rol oynayabileceği düşünülmektedir. Araştırmacılar, besin tüketiminin değerlendirilmesi konusunda bazı zorluklar [besin tüketim sıklığını veya miktarını hatırlamak gibi] yaşayabilmektedir. Besin tüketimin değerlendirilmesinde kullanılan bazı uygulamalar bu zorlukların giderilmesine katkı sağlamaktadır. Aynı zamanda araştırmacıların işini kolaylaştırmakta ve geleneksel yöntemlere göre daha güvenilir sonuçlar sağlamaktadır. Beslenme ve diyetetik alanında kullanılacak uygulamaların dezavantajlarının dikkate alınarak geliştirilmesi oldukça önemlidir. Yapay zeka uygulamalarının hem sağlığın geliştirilmesinde, hem de beslenme durumunun değerlendirmesi ve izlenmesine katkı sağlayacağı düşünülmektedir.

Anahtar kelimeler: Beslenme ve diyetetik; yapay zeka; sağlık


Bibliography:
  • Sak J, Suchodolska M. Artificial intelligence in nutrients science research: A review. Nutrients 2021; 13(2):322.
  • Nilsson NJ. The Quest for Artificial Intelligence. Cambridge University Press, 2009.
  • Matusheski NV, Caffrey A, Christensen L, Mezgec S, Surendran S, Hjorth MF, McNulty H, Pentieva K, Roager HM, Seljak BK, Vimaleswaran KS, Remmers M, Peter S. Diets, nutrients, genes and the microbiome: recent advances in personalised nutrition. British Journal of Nutrition 2021; 2021: 1-24.
  • Adams SH, Anthony JC, Carvajal R, Chae L, Khoo CSH, Latulippe ME. Perspective: Guiding principles for the implementation of personalized nutrition approaches that benefit health and function. Advances in Nutrition 2020; 11(1): 25-34.
  • Rozga M, Latulippe ME, Steiber A. Advancements in personalized nutrition technologies: Guiding principles for registered dietitian nutritionists. Journal of the Academy of Nutrition and Dietetics 2020; 120(6): 1074-1085.
  • Shim JS, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiol Health 2014; 36: e2014009.
  • Ji Y, Plourde H, Bouzo V, Kilgour RD, Cohen TR. Validity and usability of a smartphone image-based dietary assessment app compared to 3-day dood diaries in assessing dietary intake among Canadian adults: Randomized controlled trial. JMIR mHealth and uHealth 2020; 8(9): e16953.
  • Gibson RS, Charrondiere UR, Bell W. Measurement errors in dietary assessment using self-reported 24-hour recalls in low-income countries and strategies for their prevention. Advances in Nutrition 2017; 8(6): 980-991.
  • Ventura AK, Loken E, Mitchell DC, Smiciklas-Wright H, Birch LL. Understanding reporting bias in the dietary recall data of 11-year-old girls. Obesity 2006; 14(6): 1073-1084.
  • Hjartaker A, Andersen LF, Lund E. Comparison of diet measures from a food-frequency questionnaire with measures from repeated 24-hour dietary recalls. The Norwegian Women and Cancer Study: Public Health Nutrition 2007; 10(10): 1094-1103.
  • Peterson ND, Middleton KR, Nackers LM, Medina KE, Milsom VA, Perri MG. Dietary self-monitoring and long-term success with weight management. Obesity 2014; 22(9): 1962-1967.
  • Burrows TL, Ho YY, Rollo ME, Collins CE. Validity of dietary assessment methods when compared to the method of doubly labeled water: a systematic review in adults. Frontiers in Endocrinology 2019; 10: 850.
  • Lu Y, Stathopoulou T, Vasiloglou MF, Christodoulidis S, Blum B, Walser T. An artificial intelligence-based system for nutrient intake assessment of hospitalised patients. In 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society [EMBC], 2019.
  • Mezgec S, Korousic Seljak B. NutriNet: A deep learning food and drink image recognition system for dietary assessment. Nutrients 2017; 9(7): 657.
  • Eftimov T, Korosec P, Korousic Seljak B. Stand food: standardization of foods using a semi-automatic system for classifying and describing foods according to FoodEx2. Nutrients 2017; 9(6): 542.
  • Cox Sullivan S, Bopp MM, Roberson PK, Lensing S, Sullivan DH. Evaluation of an innovative method for calculating energy intake of hospitalized patients. Nutrients 2016; 8(9): 557.