Artificial Neural Network-Based Automatic Detection of Food Intake for Neuromodulation in Treating Obesity and Diabetes

Noninvasive electrical neuromodulation for gastrointestinal motility disorders

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.

Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection

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.

A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives

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.