Paper Info
Title
An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients
Abstract
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.
Year
DOI
Venue
2021
10.1109/TMM.2020.2993948
IEEE Transactions on Multimedia
Keywords
DocType
Volume
Artificial Intelligence,nutrient intake assessment,few-shot learning
Journal
23
ISSN
Citations 
PageRank 
1520-9210
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Ya Lu122.74
Thomai Stathopoulou201.35
Vasiloglou Maria F.300.34
S Christodoulidis416010.20
Zeno Stanga500.68
Stavroula G Mougiakakou634228.61