JMIR Publications

ABSTRACT

In the domain of dietary assessment, there has been an increasing amount of critique on memory-based techniques such as food frequency questionnaires or 24h 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 image recognition platforms perform and whether they could indeed be used for dietary assessment. Therefore, we conducted a performance study of currently available image recognition platforms. A variety of foods and beverages was photographed in a range of standardized settings. All pictures (n=185) were fed into 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. Top 1 accuracies ranged from 63% for Calorie Mama API® to 9% for 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. Nevertheless, important obstacles such as the accurate estimation of food quantity need to be overcome before these platforms can be used as a real alternative for traditional dietary assessment methods.