# Artificial intelligence in the diagnosis and management of dysphagia: a scoping review

## TL;DR

This review explores how artificial intelligence is being used to diagnose and manage dysphagia, focusing on the potential of deep learning techniques.

## Contribution

The study maps technological advancements in AI for dysphagia diagnosis and highlights the predominant use of deep learning methods.

## Key findings

- Deep Learning is predominantly used in AI applications for dysphagia diagnosis.
- Videofluoroscopy is the most common reference examination in these studies.
- Neurological conditions are the most prevalent among patients in the reviewed studies.

## Abstract

This scoping review aimed to map and synthesize evidence on technological advancements using Artificial Intelligence in the diagnosis and management of dysphagia. We followed the PRISMA guidelines and those of the Joanna Briggs Institute, focusing on research about technological innovations in dysphagia.

The protocol was registered on the Open Science Framework platform. The databases consulted included EMBASE, Latin American and Caribbean Health Sciences Literature (LILACS), Livivo, PubMed/Medline, Scopus, Cochrane Library, Web of Science, and grey literature.

The acronym 'PCC' was used to consider the eligibility of studies for this review.

After removing duplicates, 56 articles were initially selected. A subsequent update resulted in 205 articles, of which 61 were included after applying the selection criteria.

Videofluoroscopy of swallowing was used as the reference examination in most studies. Regarding the underlying diseases present in the patients who participated in the studies, there was a predominance of various neurological conditions. The algorithms used varied across the categories of Machine Learning, Deep Learning, and Computer Vision, with a predominance in the use of Deep Learning.

Technological advancements in artificial intelligence for the diagnosis and management of dysphagia have been mapped, highlighting the predominance and applicability of Deep Learning in examinations such as videofluoroscopy. The findings suggest significant potential to improve diagnostic accuracy and clinical management effectiveness, particularly in neurological patients. Identified research gaps require further investigations to solidify the clinical applicability and impact of these technologies.