ABSTRACT
OBJECTIVES/HYPOTHESIS: Nasopharyngeal swabs currently remain the gold standard for COVID-19 sample collection. A surge in testing volume has resulted in a large number of health care workers who are unfamiliar with nasal anatomy performing this test, which can lead to improper collection practices culminating in false-negative results and complications. Therefore, we aimed to assess the accuracy and educational potential of a realistic 3D-printed nasal swab simulator to expedite health care workers' skill acquisition. STUDY DESIGN: Prospective pre-post interventional study. METHODS: A nasal swab task trainer (NSTT) was developed to scale from computed tomography data with a deviated septum. Frontline workers at COVID-19 testing sites in Ontario, Canada, were recruited to use the NSTT for nasopharyngeal swab training. Integrated video recording capability allowed participants to self-evaluate procedure accuracy. A five-point Likert scale was collected regarding the NSTT's educational value and procedural fidelity. RESULTS: Sixty-two frontline workers included in the study were primarily registered nurses (52%) or paramedics (16%). Following simulator use, self-assessed accuracy improved in 77% of all participants and 100% of participants who expressed low confidence before training. Ninety-four percent reported that the NSTT provided a complete educational experience, and 82% regarded the system as a more effective training approach than what is currently available. Eighty-one indicated that the simulator should be used at all COVID-19 testing sites, with 77% stating province-wide implementation was warranted. CONCLUSIONS: The nasal swab task trainer is an effective educational tool that appears well-suited for improved skill acquisition in COVID-19 testing and may be useful for training other nasal swab applications. LEVEL OF EVIDENCE: 3 Laryngoscope, 2022.
ABSTRACT
BACKGROUND: The application of machine learning algorithms for assessing the auditory brainstem response has gained interest over recent years with a considerable number of publications in the literature. In this systematic review, we explore how machine learning has been used to develop algorithms to assess auditory brainstem responses. A clear and comprehensive overview is provided to allow clinicians and researchers to explore the domain and the potential translation to clinical care. METHODS: The systematic review was performed based on PRISMA guidelines. A search was conducted of PubMed, IEEE-Xplore, and Scopus databases focusing on human studies that have used machine learning to assess auditory brainstem responses. The duration of the search was from January 1, 1990, to April 3, 2021. The Covidence systematic review platform (www.covidence.org) was used throughout the process. RESULTS: A total of 5812 studies were found through the database search and 451 duplicates were removed. The title and abstract screening process further reduced the article count to 89 and in the proceeding full-text screening, 34 articles met our full inclusion criteria. CONCLUSION: Three categories of applications were found, namely neurologic diagnosis, hearing threshold estimation, and other (does not relate to neurologic or hearing threshold estimation). Neural networks and support vector machines were the most commonly used machine learning algorithms in all three categories. Only one study had conducted a clinical trial to evaluate the algorithm after development. Challenges remain in the amount of data required to train machine learning models. Suggestions for future research avenues are mentioned with recommended reporting methods for researchers.