  Regular nutrient intake monitoring in hospitalised patients plays a critical
role in reducing the risk of disease-related malnutrition (DRM). Although
several methods to estimate nutrient intake have been developed, there is still
a clear demand for a more reliable and fully automated technique, as this could
improve the data accuracy and reduce both the participant burden and the health
costs. In this paper, we propose a novel system based on artificial
intelligence to accurately estimate nutrient intake, by simply processing RGB
depth image pairs captured before and after a meal consumption. For the
development and evaluation of the system, a dedicated and new database of
images and recipes of 322 meals was assembled, coupled to data annotation using
innovative strategies. With this database, a system was developed that employed
a novel multi-task neural network and an algorithm for 3D surface construction.
This allowed sequential semantic food segmentation and estimation of the volume
of the consumed food, and permitted fully automatic estimation of nutrient
intake for each food type with a 15% estimation error.
