An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients

Lu, Ya; Stathopoulou, Thomai; Vasiloglou, Maria; Christodoulidis, Stergios; Blum, Beat; Walser, Thomas; Vinzenz, Meier; Stanga, Zeno; Mougiakakou, Stavroula (26 July 2019). An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients (In Press). In: 41st International Engineering in Medicine and Biology Conference (IEEE EMBC2019). Berlin. 23-27/07/2019.

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Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM). 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 the data accuracy and reduce both the participant burden and the health costs. In this paper, we propose a novel system based on artificial intelligence to accurately estimate nutrient intake, by simply processing RGB depth image pairs captured before and after a meal consumption. For the development and evaluation of the system, a dedicated and new database of images and recipes of 322 meals was assembled, coupled to data annotation using innovative strategies. With this database, a system was developed that employed a novel multi-task neural network and an algorithm for 3D surface construction. This allowed sequential semantic food segmentation and estimation of the volume of the consumed food, and permitted fully automatic estimation of nutrient intake for each food type with a 15% estimation error.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Stathopoulou, Thomai; Vasiloglou, Maria; Christodoulidis, Stergios; Stanga, Zeno and Mougiakakou, Stavroula

Subjects:

600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

30 Aug 2019 14:24

Last Modified:

24 Oct 2019 13:34

BORIS DOI:

10.7892/boris.132000

URI:

https://boris.unibe.ch/id/eprint/132000

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