  Regular monitoring of nutrient intake in hospitalised patients plays a
critical role in reducing the risk of disease-related malnutrition. 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 data accuracy and reduce both the burden on participants and health
costs. In this paper, we propose a novel system based on artificial
intelligence (AI) to accurately estimate nutrient intake, by simply processing
RGB Depth (RGB-D) image pairs captured before and after meal consumption. The
system includes a novel multi-task contextual network for food segmentation, a
few-shot learning-based classifier built by limited training samples for food
recognition, and an algorithm for 3D surface construction. This allows
sequential food segmentation, recognition, and estimation of the consumed food
volume, permitting fully automatic estimation of the nutrient intake for each
meal. For the development and evaluation of the system, a dedicated new
database containing images and nutrient recipes of 322 meals is assembled,
coupled to data annotation using innovative strategies. Experimental results
demonstrate that the estimated nutrient intake is highly correlated (> 0.91) to
the ground truth and shows very small mean relative errors (< 20%),
outperforming existing techniques proposed for nutrient intake assessment.
