TITLE: Applications based on Artificial Intelligence for swallowing analysis described in the literature: Scope Review
AUTHORS: maiara tomanchieviez, Rafaela Soares Rech, Fernando Neves Hugo

[DESCRIPTION]
Introduction: Considering the growing number of articles involving artificial intelligence strategies for analyzing the biomechanics of swallowing, understanding which mechanisms are being described in the studies and how the methodologies are outlined is essential to evaluate the real clinical application of these processes. Given the heterogeneity of studies found in the literature to provide an overview of this field, we considered carrying out a scoping review. The objective of this publication is to present a scoping review protocol on which applications based on Artificial Intelligence for swallowing analysis are described in the literature. Methods and analysis: The review will follow the Joanna Briggs Institute Scoping Review Protocol. The databases used for research will be Pubmed, Embase, Scopus and Web Of Science, in addition to gray literature using Google Scholar. Two independent reviewers will select the articles and disagreements will be resolved by a third reviewer. The results will be analyzed using descriptive statistics. Ethics and dissemination: An article will be published with the results of this review in a peer-reviewed journal. This study does not require approval from the research ethics committee.

[GUIDELINES]
Strategies involving Artificial Intelligence (AI) and machine learning have grown exponentially in healthcare practices, proposing new assessment, diagnosis and intervention strategies[1]. In medicine, its applicability can be virtual or physical, covering control of health management systems including electronic health records, patient support robots and even robotic surgery systems, for example[2].
The use of AI is promising and can help expand access and quality of care, increase the efficiency and safety of services provided, train different actors in the clinical process, support research and innovation, increase health sustainability and ecological responsibility[ 3,4].
In speech therapy, studies involving machine learning are recent and demonstrate that these strategies are capable of contributing to the assessment and diagnosis of disorders in the areas of voice[5], language[6], cognition[7] and swallowing[8,9, 10]. AI can help by minimizing the weaknesses harshly criticized in traditional speech therapy, which is too subjective and dependent on the professional's clinical experience.
Regarding the biomechanics of swallowing, articles have been published relating AI-based applications to the various parameters of its assessment, such as swallowing sounds[8, 9, 10], laryngeal movements and respiratory flow[11], in subjects with pathologies and without [12] and in controlled situations [10] or close to everyday life [14]. It is expected to speed up care, freeing the workforce from laborious routine tasks, increasing access to healthcare, reducing costs and facilitating personalized, predictive and participatory speech therapy.
In order to unify the evidence found to provide an overview of these AI processing strategies for swallowing assessments, a scoping review will be carried out. Scoping reviews are a recent type of knowledge synthesis that systematically maps the evidence on a specific topic, identifying concepts, theories, sources of evidence and research. The methodological protocol followed by this work will be the Joanna Briggs Institute guidelines. Therefore, this document presents a scoping review protocol whose objective is to map the applications based on Artificial Intelligence for swallowing analysis described in the literature.

[STEPS]