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

Lu, Ya; Stathopoulou, Thomai; Vasiloglou, Maria F.; Christodoulidis, Stergios; Stanga, Zeno; Mougiakakou, Stavroula (2020). An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients. IEEE transactions on multimedia, 2019, pp. 5696-5699. Institute of Electrical and Electronics Engineers 10.1109/TMM.2020.2993948

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

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

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Lu, Ya; Stathopoulou, Thomai; Vasiloglou, Maria; Christodoulidis, Stergios; Stanga-Nodari, Zeno and Mougiakakou, Stavroula

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

1520-9210

Publisher:

Institute of Electrical and Electronics Engineers

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

26 May 2020 16:05

Last Modified:

31 May 2020 02:41

Publisher DOI:

10.1109/TMM.2020.2993948

PubMed ID:

31947145

Uncontrolled Keywords:

Artificial Intelligence; nutrient intake assessment; few-shot learning

URI:

https://boris.unibe.ch/id/eprint/139352

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