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

• URL: http://arxiv.org/abs/2003.08273v1
• Date: Wed, 18 Mar 2020 15:28:51 GMT
• Title: An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients
• Authors: Ya Lu, Thomai Stathopoulou, Maria F. Vasiloglou, Stergios Christodoulidis, Zeno Stanga, Stavroula Mougiakakou
• Abstract summary: Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition. We propose a novel system based on artificial intelligence (AI) to accurately estimate nutrient intake.
• Score: 4.048427587958764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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