Evidence map›Paper›PMID 34960091›Full record

ArticleNutrients2021

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

Ioannis Papathanail, Jana Brühlmann, Maria F Vasiloglou, Thomai Stathopoulou, Aristomenis K Exadaktylos, Zeno Stanga, Thomas Münzer, Stavroula Mougiakakou

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nutrients, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 3 pooled it
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 36 citations in OpenAlex.

  1. Pooled it
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  5. Observational
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  14. Multimedia Data-Based Mobile Applications for Dietary Assessment.Journal of diabetes science and technology · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Ioannis PapathanailARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland.
Jana BrühlmannGeriatrische Klinik St. Gallen AG, Rorschacherstrasse 94, 9000 St. Gallen, Switzerland.
Maria F VasiloglouARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland.
Thomai StathopoulouARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland.
Aristomenis K ExadaktylosDepartment of Emergency Medicine, Bern University Hospital, University of Bern, 3010 Bern, Switzerland.
Zeno StangaDepartment of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland.
Thomas MünzerGeriatrische Klinik St. Gallen AG, Rorschacherstrasse 94, 9000 St. Gallen, Switzerland.ORCID 0000-0003-1891-1171
Stavroula MougiakakouARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland.
University of Bern · CHUniversity of St.Gallen · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceEnergy IntakeImage Processing, Computer-AssistedNutrition AssessmentAgedDietEatingFeeding BehaviorHospitalizationHumansInpatientsMalnutritionMealsartificial intelligencedietary assessmentdietary intakegeriatricsmalnutrition

Identifiers

PMID34960091
PMCPMC8706142
OpenAlexW4200283107

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.