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Pucci G, Grillo A, Dalakleidi KV, Fraenkel E, Gkaliagkousi E, Golemati S, Guala A, Hametner B, Lazaridis A, Mayer CC, Mozos I, Pereira T, Veerasingam D, Terentes-Printzios D, Agnoletti D. Atrial Fibrillation and Early Vascular Aging: Clinical Implications, Methodology Issues and Open Questions-A Review from the VascAgeNet COST Action. J Clin Med 2024; 13:1207. [PMID: 38592046 PMCID: PMC10931681 DOI: 10.3390/jcm13051207] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 04/10/2024] Open
Abstract
Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with adverse CV outcomes. Vascular aging (VA), which is defined as the progressive deterioration of arterial function and structure over a lifetime, is an independent predictor of both AF development and CV events. A timing identification and treatment of early VA has therefore the potential to reduce the risk of AF incidence and related CV events. A network of scientists and clinicians from the COST Action VascAgeNet identified five clinically and methodologically relevant questions regarding the relationship between AF and VA and conducted a narrative review of the literature to find potential answers. These are: (1) Are VA biomarkers associated with AF? (2) Does early VA predict AF occurrence better than chronological aging? (3) Is early VA a risk enhancer for the occurrence of CV events in AF patients? (4) Are devices measuring VA suitable to perform subclinical AF detection? (5) Does atrial-fibrillation-related rhythm irregularity have a negative impact on the measurement of vascular age? Results showed that VA is a powerful and independent predictor of AF incidence, however, its role as risk modifier for the occurrence of CV events in patients with AF is debatable. Limited and inconclusive data exist regarding the reliability of VA measurement in the presence of rhythm irregularities associated with AF. To date, no device is equipped with tools capable of detecting AF during VA measurements. This represents a missed opportunity to effectively perform CV prevention in people at high risk. Further advances are needed to fill knowledge gaps in this field.
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Affiliation(s)
- Giacomo Pucci
- Unit of Internal Medicine, Santa Maria University Hospital, 05100 Terni, Italy
- Department of Medicine and Surgery, University of Perugia, 06125 Perugia, Italy
| | - Andrea Grillo
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34149 Trieste, Italy
| | - Kalliopi V Dalakleidi
- Biomedical Simulations and Imaging (BIOSIM) Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
| | - Emil Fraenkel
- 1st Department of Internal Medicine, Faculty of General Medicine, Pavol Jozef Šafárik University, 04011 Košice, Slovakia
| | - Eugenia Gkaliagkousi
- 3rd Department of Internal Medicine, Aristotle University of Thessaloniki, Papageorgiou General Hospital, 54124 Thessaloniki, Greece
| | - Spyretta Golemati
- Medical School, National and Kapodistrian University of Athens, 10675 Athens, Greece
| | - Andrea Guala
- Vall d'Hebrón Research Institute (VHIR), 08035 Barcelona, Spain
- CIBER CV, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Bernhard Hametner
- AIT Austrian Institute of Technology, Center for Health & Bioresources, Medical Signal Analysis, 1210 Vienna, Austria
| | - Antonios Lazaridis
- 3rd Department of Internal Medicine, Aristotle University of Thessaloniki, Papageorgiou General Hospital, 54124 Thessaloniki, Greece
| | - Christopher C Mayer
- AIT Austrian Institute of Technology, Center for Health & Bioresources, Medical Signal Analysis, 1210 Vienna, Austria
| | - Ioana Mozos
- Department of Functional Sciences-Pathophysiology, Center for Translational Research and Systems Medicine, "Victor Babes" University of Medicine and Pharmacy, 300173 Timisoara, Romania
| | - Telmo Pereira
- H&TRC-Health & Technology Research Center, Coimbra Health School, Polytechnic University of Coimbra, 3000-331 Coimbra, Portugal
- Laboratory for Applied Research in Health (Labinsaúde), Polytechnic University of Coimbra, 3000-331 Coimbra, Portugal
| | - Dave Veerasingam
- Department of Cardiothoracic Surgery, Galway University Hospitals, H91 YR71 Galway, Ireland
| | - Dimitrios Terentes-Printzios
- First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Davide Agnoletti
- Cardiovascular Internal Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Cardiovascular Internal Medicine, Medical and Surgical Sciences Department, University of Bologna, 40138 Bologna, Italy
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Dalakleidi KV, Papadelli M, Kapolos I, Papadimitriou K. Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review. Adv Nutr 2022; 13:2590-2619. [PMID: 35803496 PMCID: PMC9776640 DOI: 10.1093/advances/nmac078] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/06/2022] [Accepted: 07/06/2022] [Indexed: 01/29/2023] Open
Abstract
Dietary assessment can be crucial for the overall well-being of humans and, at least in some instances, for the prevention and management of chronic, life-threatening diseases. Recall and manual record-keeping methods for food-intake monitoring are available, but often inaccurate when applied for a long period of time. On the other hand, automatic record-keeping approaches that adopt mobile cameras and computer vision methods seem to simplify the process and can improve current human-centric diet-monitoring methods. Here we present an extended critical literature overview of image-based food-recognition systems (IBFRS) combining a camera of the user's mobile device with computer vision methods and publicly available food datasets (PAFDs). In brief, such systems consist of several phases, such as the segmentation of the food items on the plate, the classification of the food items in a specific food category, and the estimation phase of volume, calories, or nutrients of each food item. A total of 159 studies were screened in this systematic review of IBFRS. A detailed overview of the methods adopted in each of the 78 included studies of this systematic review of IBFRS is provided along with their performance on PAFDs. Studies that included IBFRS without presenting their performance in at least 1 of the above-mentioned phases were excluded. Among the included studies, 45 (58%) studies adopted deep learning methods and especially convolutional neural networks (CNNs) in at least 1 phase of the IBFRS with input PAFDs. Among the implemented techniques, CNNs outperform all other approaches on the PAFDs with a large volume of data, since the richness of these datasets provides adequate training resources for such algorithms. We also present evidence for the benefits of application of IBFRS in professional dietetic practice. Furthermore, challenges related to the IBFRS presented here are also thoroughly discussed along with future directions.
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Affiliation(s)
- Kalliopi V Dalakleidi
- Department of Food Science and Technology, University of the Peloponnese, Kalamata, Greece
| | - Marina Papadelli
- Department of Food Science and Technology, University of the Peloponnese, Kalamata, Greece
| | - Ioannis Kapolos
- Department of Food Science and Technology, University of the Peloponnese, Kalamata, Greece
| | - Konstantinos Papadimitriou
- Laboratory of Food Quality Control and Hygiene, Department of Food Science and Human Nutrition, Agricultural University of Athens, Athens, Greece
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Fragkiadakis E, Dalakleidi KV, Nikita KS. Design and Development of a Sitting Posture Recognition System. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2019:3364-3367. [PMID: 31946602 DOI: 10.1109/embc.2019.8856635] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Sitting posture recognition can be used to evaluate the awareness of a person carrying out a task, such as working or driving, and can aid in avoiding accidents or other health risks, such as musculoskeletal disorders. In addition, sitting posture can reveal wellness or unhealthiness for the elderly and mobility disabled individuals. This paper focuses on body posture monitoring, by acquiring the pressure distribution of a sitting person with thirteen piezoresistive sensors placed on a seat. The measurements from the sensors passing through a microcontroller unit fed several machine learning techniques in order to discriminate among five sitting postures (upright, leaning left, leaning right, leaning forward and leaning backward). Experiments with body postures from twelve individuals (six men and six women) of different Body Mass Index (underweight, normal and overweight) were conducted. The developed classifiers achieved average discrimination accuracy over 98% among the aforementioned five body postures.
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