Acurateţea inteligenţei artificiale în analiza imaginilor cu alimente

 Accuracy of artificial intelligence in food image analysis

First published: 28 iunie 2024

Editorial Group: MEDICHUB MEDIA

DOI: 10.26416/JourNutri.2.2.2024.9766


Assessing the nutritional values of meals is important for the public health. Current evaluation methods can be imprecise due to human errors, but food image ana­ly­sis, facilitated by artificial intelligence (AI), can en­hance determination accuracy. This study examines the accuracy of GPT-4 in identifying foods and estimating nu­tri­tio­nal values from simple images. Methodology. Three images of culinary dishes were used. A customized GPT model was engaged to identify the foods and esti­mate their nutritional values. The results were compared with those from the Nutrition ARTS platform, which uses a USDA database. Results. The GPT model correctly iden­ti­fied the foods in all images. The estimated calorie va­lues had an average difference of ±5 kcal compared to the reference. Differences for proteins were ±4.4 g, for car­bo­hy­drates ±1.3 g, and for fats ±0.7 g. Differences in micronutrient values were higher. Conclusions. The GPT model accurately identified foods and estimated ca­lo­ries and macronutrients. However, differences in micronutrient values were significant. The study high­lighted the potential and limitations of AI in food image ana­ly­sis. Further research with a larger dataset could pro­vide more relevant information.

nutritional analysis, food images, artificial intelligence, nutritional values


Determinarea valorilor nutriţionale ale meselor este im­por­tan­tă pentru sănătatea publică. Metodele actuale de evaluare pot fi imprecise din cauza erorilor umane, dar analiza ima­gi­ni­lor cu alimente, facilitată de inteligenţa artificială (IA), poate spo­ri precizia determinărilor. Studiul de faţă examinează acu­ra­te­ţea GPT-4 în identificarea alimentelor şi estimarea va­lo­ri­lor nu­tri­ţio­nale din imagini simple. Metodologie. S-au utilizat trei imagini cu preparate culinare. Un model per­so­na­li­zat GPT a fost folosit pentru a identifica alimentele şi a estima va­lo­ri­le nutriţionale. Rezultatele au fost comparate cu cele ale platformei Nutrition ARTS, care foloseşte o bază de date a USDA. Rezultate. Modelul GPT a identificat corect alimentele din toate imaginile. Valorile caloriilor estimate au avut o di­fe­ren­ţă medie de ±5 kcal faţă de referinţă. Diferenţele pen­tru proteine au fost de ±4,4 g, pentru glucide de ±1,3 g şi pen­tru lipide de ±0,7 g. La micronutrienţi, diferenţele au fost mai crescute. Concluzii. Modelul GPT a demonstrat o bună acu­ra­te­ţe în identificarea alimentelor şi estimarea caloriilor şi macronutrienţilor. Totuşi, pentru micronutrienţi, diferenţele au fost semnificative. Studiul a evidenţiat potenţialul, dar şi limitările IA în analiza imaginilor cu alimente. Cercetări ul­te­rioa­re, pe un set mai mare de date, vor putea oferi informaţii mai relevante.


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