Validity of nutrient and food group contents estimated automatically by an image recognition system: A smartphone application for health promotion (Preprint)
BACKGROUND A smartphone image recognition application is expected to be a novel tool to measure nutrients and food intake, but its performance has not been well evaluated. OBJECTIVE We assessed the performance of an image recognition application called CALO mama in terms of the nutrient and food group contents automatically estimated by the application. METHODS We prepared 120 sample meals for which the nutrients and food groups were already calculated. Next, we predicted the nutrients and food groups included in the meals from their photographs using 1) automated image recognition only and 2) manual modification after automatic identification. RESULTS Predictions using only image recognition were similar to the actual data in weight of meals, 11 out of 30 nutrients, and 4 out of 15 food groups; it underestimated energy, 19 nutrients, and 9 food groups; it overestimated dairy products and confectioneries. After manual modification, predictions were similar in energy, 29 out of 30 nutrients, and 10 out of 15 food groups; it underestimated pulses, fruits, and meats; it overestimated weight, vitamin C, vegetables, and confectioneries. CONCLUSIONS The results of this study suggest that manual modification after prediction using image recognition improves the performance of the assessment of nutrients and food intake. Our findings suggest the potential of image recognition to achieve a description of the dietary intakes of populations using “precision nutrition” (a comprehensive and dynamic approach to develop tailored nutritional recommendations) for individuals.