An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients
This addresses the need for reliable, automated monitoring to reduce malnutrition risk and healthcare costs in hospital settings, representing an incremental improvement over existing methods.
The paper tackles the problem of automating nutrient intake assessment for hospitalized patients by proposing an AI system that processes RGB-D images before and after meals to estimate food volume and nutrient intake, achieving high correlation (>0.91) with ground truth and low mean relative errors (<20%).
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.