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Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients

1
ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
2
Geriatrische Klinik St. Gallen AG, Rorschacherstrasse 94, 9000 St. Gallen, Switzerland
3
Department of Emergency Medicine, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
4
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Keisuke Maeda
Nutrients 2021, 13(12), 4539; https://doi.org/10.3390/nu13124539
Received: 8 November 2021 / Revised: 9 December 2021 / Accepted: 14 December 2021 / Published: 17 December 2021
(This article belongs to the Section Geriatric Nutrition)
Malnutrition is common, especially among older, hospitalised patients, and is associated with higher mortality, longer hospitalisation stays, infections, and loss of muscle mass. It is therefore of utmost importance to employ a proper method for dietary assessment that can be used for the identification and management of malnourished hospitalised patients. In this study, we propose an automated Artificial Intelligence (AI)-based system that receives input images of the meals before and after their consumption and is able to estimate the patient’s energy, carbohydrate, protein, fat, and fatty acids intake. The system jointly segments the images into the different food components and plate types, estimates the volume of each component before and after consumption, and calculates the energy and macronutrient intake for every meal, based on the kitchen’s menu database. Data acquired from an acute geriatric hospital as well as from our previous study were used for the fine-tuning and evaluation of the system. The results from both our system and the hospital’s standard procedure were compared to the estimations of experts. Agreement was better with the system, suggesting that it has the potential to replace standard clinical procedures with a positive impact on time spent directly with the patients. View Full-Text
Keywords: dietary assessment; artificial intelligence; dietary intake; geriatrics; malnutrition dietary assessment; artificial intelligence; dietary intake; geriatrics; malnutrition
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MDPI and ACS Style

Papathanail, I.; Brühlmann, J.; Vasiloglou, M.F.; Stathopoulou, T.; Exadaktylos, A.K.; Stanga, Z.; Münzer, T.; Mougiakakou, S. Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients. Nutrients 2021, 13, 4539. https://doi.org/10.3390/nu13124539

AMA Style

Papathanail I, Brühlmann J, Vasiloglou MF, Stathopoulou T, Exadaktylos AK, Stanga Z, Münzer T, Mougiakakou S. Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients. Nutrients. 2021; 13(12):4539. https://doi.org/10.3390/nu13124539

Chicago/Turabian Style

Papathanail, Ioannis, Jana Brühlmann, Maria F. Vasiloglou, Thomai Stathopoulou, Aristomenis K. Exadaktylos, Zeno Stanga, Thomas Münzer, and Stavroula Mougiakakou. 2021. "Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients" Nutrients 13, no. 12: 4539. https://doi.org/10.3390/nu13124539

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