TY  - JOUR
AU  - Van Asbroeck, Stephanie
AU  - Matthys, Christophe
PY  - 2020
DA  - 2020/12/7
TI  - Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study
JO  - JMIR Form Res
SP  - e15602
VL  - 4
IS  - 12
KW  - image recognition
KW  - dietary assessment
KW  - automated food recognition
KW  - accuracy
AB  - Background: In the domain of dietary assessment, there has been an increasing amount of criticism of memory-based techniques such as food frequency questionnaires or 24 hour recalls. One alternative is logging pictures of consumed food followed by an automatic image recognition analysis that provides information on type and amount of food in the picture. However, it is currently unknown how well commercial image recognition platforms perform and whether they could indeed be used for dietary assessment. Objective: This is a comparative performance study of commercial image recognition platforms. Methods: A variety of foods and beverages were photographed in a range of standardized settings. All pictures (n=185) were uploaded to selected recognition platforms (n=7), and estimates were saved. Accuracy was determined along with totality of the estimate in the case of multiple component dishes. Results: Top 1 accuracies ranged from 63% for the application programming interface (API) of the Calorie Mama app to 9% for the Google Vision API. None of the platforms were capable of estimating the amount of food. These results demonstrate that certain platforms perform poorly while others perform decently. Conclusions: Important obstacles to the accurate estimation of food quantity need to be overcome before these commercial platforms can be used as a real alternative for traditional dietary assessment methods. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2020/12/e15602
UR  - https://doi.org/10.2196/15602
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33284118
DO  - 10.2196/15602
ID  - info:doi/10.2196/15602
ER  -