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Papathanail I, Abdur Rahman L, Brigato L, Bez NS, Vasiloglou MF, van der Horst K, Mougiakakou S. The Nutritional Content of Meal Images in Free-Living Conditions-Automatic Assessment with goFOOD TM. Nutrients 2023; 15:3835. [PMID: 37686866 PMCID: PMC10490087 DOI: 10.3390/nu15173835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023] Open
Abstract
A healthy diet can help to prevent or manage many important conditions and diseases, particularly obesity, malnutrition, and diabetes. Recent advancements in artificial intelligence and smartphone technologies have enabled applications to conduct automatic nutritional assessment from meal images, providing a convenient, efficient, and accurate method for continuous diet evaluation. We now extend the goFOODTM automatic system to perform food segmentation, recognition, volume, as well as calorie and macro-nutrient estimation from single images that are captured by a smartphone. In order to assess our system's performance, we conducted a feasibility study with 50 participants from Switzerland. We recorded their meals for one day and then dietitians carried out a 24 h recall. We retrospectively analysed the collected images to assess the nutritional content of the meals. By comparing our results with the dietitians' estimations, we demonstrated that the newly introduced system has comparable energy and macronutrient estimation performance with the previous method; however, it only requires a single image instead of two. The system can be applied in a real-life scenarios, and it can be easily used to assess dietary intake. This system could help individuals gain a better understanding of their dietary consumption. Additionally, it could serve as a valuable resource for dietitians, and could contribute to nutritional research.
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Affiliation(s)
- Ioannis Papathanail
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland; (I.P.); (L.A.R.); (L.B.); (M.F.V.)
| | - Lubnaa Abdur Rahman
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland; (I.P.); (L.A.R.); (L.B.); (M.F.V.)
| | - Lorenzo Brigato
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland; (I.P.); (L.A.R.); (L.B.); (M.F.V.)
| | - Natalie S. Bez
- School of Health Professions, Bern University of Applied Sciences, 3008 Bern, Switzerland; (N.S.B.); (K.v.d.H.)
| | - Maria F. Vasiloglou
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland; (I.P.); (L.A.R.); (L.B.); (M.F.V.)
| | - Klazine van der Horst
- School of Health Professions, Bern University of Applied Sciences, 3008 Bern, Switzerland; (N.S.B.); (K.v.d.H.)
| | - Stavroula Mougiakakou
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland; (I.P.); (L.A.R.); (L.B.); (M.F.V.)
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Abstract
Diabetes mellitus (DM) and obesity are chronic medical conditions associated with significant morbidity and mortality. Accurate macronutrient and energy estimation could be beneficial in attempts to manage DM and obesity, leading to improved glycemic control and weight reduction, respectively. Existing dietary assessment methods are subject to major errors in measurement, are time consuming, are costly, and do not provide real-time feedback. The increasing adoption of smartphones and artificial intelligence, along with the advances in algorithms and hardware, allowed the development of technologies executed in smartphones that use food/beverage multimedia data as an input, and output information about the nutrient content in almost real time. Scope of this review was to explore the various image-based and video-based systems designed for dietary assessment. We identified 22 different systems and divided these into three categories on the basis of their setting for evaluation: laboratory (12), preclinical (7), and clinical (3). The major findings of the review are that there is still a number of open research questions and technical challenges to be addressed and end users-including health care professionals and patients-need to be involved in the design and development of such innovative solutions. Last, there is a clear need that these systems should be validated under unconstrained real-life conditions and that they should be compared with conventional methods for dietary assessment.
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Affiliation(s)
- Maria F Vasiloglou
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Isabel Marcano
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Sergio Lizama
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ioannis Papathanail
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Elias K Spanakis
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
- Division of Endocrinology, Baltimore Veterans Affairs Medical Center, Baltimore, MD, USA
| | - Stavroula Mougiakakou
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Emergency Medicine, Bern University Hospital, Bern, Switzerland
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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; 13:4539. [PMID: 34960091 PMCID: PMC8706142 DOI: 10.3390/nu13124539] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/09/2021] [Accepted: 12/14/2021] [Indexed: 01/07/2023] Open
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.
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Affiliation(s)
- Ioannis Papathanail
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland; (I.P.); (M.F.V.); (T.S.)
| | - Jana Brühlmann
- Geriatrische Klinik St. Gallen AG, Rorschacherstrasse 94, 9000 St. Gallen, Switzerland; (J.B.); (T.M.)
| | - Maria F. Vasiloglou
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland; (I.P.); (M.F.V.); (T.S.)
| | - Thomai Stathopoulou
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland; (I.P.); (M.F.V.); (T.S.)
| | | | - Zeno Stanga
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland;
| | - Thomas Münzer
- Geriatrische Klinik St. Gallen AG, Rorschacherstrasse 94, 9000 St. Gallen, Switzerland; (J.B.); (T.M.)
| | - Stavroula Mougiakakou
- ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland; (I.P.); (M.F.V.); (T.S.)
- Department of Emergency Medicine, Bern University Hospital, University of Bern, 3010 Bern, Switzerland;
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Vasiloglou MF, Lu Y, Stathopoulou T, Papathanail I, Faeh D, Ghosh A, Baumann M, Mougiakakou S. Assessing Mediterranean Diet Adherence with the Smartphone: The Medipiatto Project. Nutrients 2020; 12:nu12123763. [PMID: 33297550 PMCID: PMC7762404 DOI: 10.3390/nu12123763] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/01/2020] [Accepted: 12/02/2020] [Indexed: 02/06/2023] Open
Abstract
The Mediterranean diet (MD) is regarded as a healthy eating pattern with beneficial effects both for the decrease of the risk for non-communicable diseases and also for body weight reduction. In the current manuscript, we propose an automated smartphone application which monitors and evaluates the user’s adherence to MD using images of the food and drinks that they consume. We define a set of rules for automatic adherence estimation, which focuses on the main MD food groups. We use a combination of a convolutional neural network (CNN) and a graph convolutional network to detect the types of foods and quantities from the users’ food images and the defined set of rules to evaluate the adherence to MD. Our experiments show that our system outperforms a basic CNN in terms of recognizing food items and estimating quantity and yields comparable results as experienced dietitians when it comes to overall MD adherence estimation. As the system is novel, these results are promising; however, there is room for improvement of the accuracy by gathering and training with more data and certain refinements can be performed such as re-defining the set of rules to also be able to be used for sub-groups of MD (e.g., vegetarian type of MD).
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Affiliation(s)
- Maria F. Vasiloglou
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland; (M.F.V.); (Y.L.); (T.S.); (I.P.)
| | - Ya Lu
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland; (M.F.V.); (Y.L.); (T.S.); (I.P.)
| | - Thomai Stathopoulou
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland; (M.F.V.); (Y.L.); (T.S.); (I.P.)
| | - Ioannis Papathanail
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland; (M.F.V.); (Y.L.); (T.S.); (I.P.)
| | - David Faeh
- Epidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, 8001 Zurich, Switzerland;
| | - Arindam Ghosh
- Oviva S.A., 8852 Altendorf, Switzerland; (A.G.); (M.B.)
| | | | - Stavroula Mougiakakou
- ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland; (M.F.V.); (Y.L.); (T.S.); (I.P.)
- Correspondence: ; Tel.: +41-31-632-7592
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