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Yin J, Chen JD. Noninvasive electrical neuromodulation for gastrointestinal motility disorders. Expert Rev Gastroenterol Hepatol 2023; 17:1221-1232. [PMID: 38018087 PMCID: PMC10842705 DOI: 10.1080/17474124.2023.2288156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 11/22/2023] [Indexed: 11/30/2023]
Abstract
INTRODUCTION Gastrointestinal motility disorders are highly prevalent without satisfactory treatment. noninvasive electrical neuromodulation is an emerging therapy for treating various gastrointestinal motility disorders. AREAS COVERED In this review, several emerging noninvasive neuromodulation methods are introduced, including transcutaneous auricular vagal nerve stimulation, percutaneous auricular vagal nerve stimulation, transcutaneous cervical vagal nerve stimulation, transcutaneous electrical acustimulation, transabdominal interference stimulation, tibial nerve stimulation, and translumbosacral neuromodulation therapy. Their clinical applications in the most common gastrointestinal motility are discussed, including gastroesophageal reflux disease, functional dyspepsia, gastroparesis, functional constipation, irritable bowel syndrome, and fecal incontinence. PubMed database was searched from 1995 to June 2023 for relevant articles in English. EXPERT OPINION Noninvasive neuromodulation is effective and safe in improving both gastrointestinal symptoms and dysmotility; it can be used when pharmacotherapy is ineffective. Future directions include refining the methodology, improving device development and understanding mechanisms of action.
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Affiliation(s)
- Jieyun Yin
- Transtimulation Research Inc, Oklahoma City, OK, USA
| | - Jiande Dz Chen
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor MI, USA
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Neves PA, Simões J, Costa R, Pimenta L, Gonçalves NJ, Albuquerque C, Cunha C, Zdravevski E, Lameski P, Garcia NM, Pires IM. Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection. SENSORS (BASEL, SWITZERLAND) 2022; 22:6443. [PMID: 36080901 PMCID: PMC9460522 DOI: 10.3390/s22176443] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/14/2022] [Accepted: 08/24/2022] [Indexed: 06/15/2023]
Abstract
Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer's disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.
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Affiliation(s)
- Paulo Alexandre Neves
- School of Technology, Polytechnic Institute of Castelo Branco, 6000-767 Castelo Branco, Portugal
| | - João Simões
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Ricardo Costa
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Luís Pimenta
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Norberto Jorge Gonçalves
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Carlos Albuquerque
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3046-851 Coimbra, Portugal
- Higher School of Health, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
- Child Studies Research Center (CIEC), University of Minho, 4710-057 Braga, Portugal
| | - Carlos Cunha
- CISeD—Research Centre in Digital Services, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia
| | - Petre Lameski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia
| | - Nuno M. Garcia
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
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A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives. Obes Surg 2021; 31:4555-4563. [PMID: 34264433 DOI: 10.1007/s11695-021-05548-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/12/2021] [Accepted: 06/17/2021] [Indexed: 01/01/2023]
Abstract
Artificial intelligence (AI) is a revolution in data analysis with emerging roles in various specialties and with various applications. The objective of this scoping review was to retrieve current literature on the fields of AI that have been applied to metabolic bariatric surgery (MBS) and to investigate potential applications of AI as a decision-making tool of the bariatric surgeon. Initial search yielded 3260 studies published from January 2000 until March 2021. After screening, 49 unique articles were included in the final analysis. Studies were grouped into categories, and the frequency of appearing algorithms, dataset types, and metrics were documented. The heterogeneity of current studies showed that meticulous validation, strict reporting systems, and reliable benchmarking are mandatory for ensuring the clinical validity of future research.
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