An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients

• Published in: IEEE transactions on multimedia Vol. 23; pp. 1136 - 1147
• Main Authors: Lu, Ya, Stathopoulou, Thomai, Vasiloglou, Maria F., Christodoulidis, Stergios, Stanga, Zeno, Mougiakakou, Stavroula
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Annotations; Artificial intelligence; Estimation; few-shot learning; Food; Food intake; Hospitals; Image segmentation; Meals; Medical imaging; nutrient intake assessment; Recognition; Training data; Visualization
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2020.2993948

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.