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Crystal AA, Valero M, Nino V, Ingram KH. Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation. SENSORS (BASEL, SWITZERLAND) 2024; 24:4089. [PMID: 39000868 PMCID: PMC11244259 DOI: 10.3390/s24134089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/06/2024] [Accepted: 06/14/2024] [Indexed: 07/16/2024]
Abstract
Diabetes has emerged as a worldwide health crisis, affecting approximately 537 million adults. Maintaining blood glucose requires careful observation of diet, physical activity, and adherence to medications if necessary. Diet monitoring historically involves keeping food diaries; however, this process can be labor-intensive, and recollection of food items may introduce errors. Automated technologies such as food image recognition systems (FIRS) can make use of computer vision and mobile cameras to reduce the burden of keeping diaries and improve diet tracking. These tools provide various levels of diet analysis, and some offer further suggestions for improving the nutritional quality of meals. The current study is a systematic review of mobile computer vision-based approaches for food classification, volume estimation, and nutrient estimation. Relevant articles published over the last two decades are evaluated, and both future directions and issues related to FIRS are explored.
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Affiliation(s)
- Afnan Ahmed Crystal
- Department of Computer Science, Kennesaw State University, Kennesaw, GA 30060, USA
| | - Maria Valero
- Department of Information Technology, Kennesaw State University, Kennesaw, GA 30060, USA
| | - Valentina Nino
- Departement of Industrial and Systems Engineering, Kennesaw State University, Kennesaw, GA 30060, USA
| | - Katherine H Ingram
- Department of Exercise Science and Sport Management, Kennesaw State University, Kennesaw, GA 30060, USA
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Vasiloglou MF, Marcano I, Lizama S, Papathanail I, Spanakis EK, Mougiakakou S. Multimedia Data-Based Mobile Applications for Dietary Assessment. J Diabetes Sci Technol 2022:19322968221085026. [PMID: 35348398 DOI: 10.1177/19322968221085026] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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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Chen J, Zhu B, Ngo CW, Chua TS, Jiang YG. A Study of Multi-Task and Region-Wise Deep Learning for Food Ingredient Recognition. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2020; 30:1514-1526. [PMID: 33360994 DOI: 10.1109/tip.2020.3045639] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Food recognition has captured numerous research attention for its importance for health-related applications. The existing approaches mostly focus on the categorization of food according to dish names, while ignoring the underlying ingredient composition. In reality, two dishes with the same name do not necessarily share the exact list of ingredients. Therefore, the dishes under the same food category are not mandatorily equal in nutrition content. Nevertheless, due to limited datasets available with ingredient labels, the problem of ingredient recognition is often overlooked. Furthermore, as the number of ingredients is expected to be much less than the number of food categories, ingredient recognition is more tractable in the real-world scenario. This paper provides an insightful analysis of three compelling issues in ingredient recognition. These issues involve recognition in either image-level or region level, pooling in either single or multiple image scales, learning in either single or multi-task manner. The analysis is conducted on a large food dataset, Vireo Food-251, contributed by this paper. The dataset is composed of 169,673 images with 251 popular Chinese food and 406 ingredients. The dataset includes adequate challenges in scale and complexity to reveal the limit of the current approaches in ingredient recognition.
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Jim HSL, Hoogland AI, Brownstein NC, Barata A, Dicker AP, Knoop H, Gonzalez BD, Perkins R, Rollison D, Gilbert SM, Nanda R, Berglund A, Mitchell R, Johnstone PAS. Innovations in research and clinical care using patient-generated health data. CA Cancer J Clin 2020; 70:182-199. [PMID: 32311776 PMCID: PMC7488179 DOI: 10.3322/caac.21608] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 02/24/2020] [Accepted: 02/24/2020] [Indexed: 12/17/2022] Open
Abstract
Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.
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Affiliation(s)
- Heather S L Jim
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida
| | - Aasha I Hoogland
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida
| | - Naomi C Brownstein
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida
| | - Anna Barata
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Hans Knoop
- Department of Medical Psychology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Brian D Gonzalez
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, Florida
| | - Randa Perkins
- Department of Clinical Informatics and Clinical Systems, Moffitt Cancer Center, Tampa, Florida
| | - Dana Rollison
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida
| | - Scott M Gilbert
- Department of Genitourinary Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Ronica Nanda
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida
- BayCare Health Systems Inc, Morton Plant Hospital, Clearwater, Florida
| | - Anders Berglund
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida
| | - Ross Mitchell
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida
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