Machine learning approaches used to analyze auditory evoked responses from the human auditory brainstem: A systematic review. (November 2022) - Wimalarathna, Hasitha; Ankmnal-Veeranna, Sangamanatha; Allan, Chris; Agrawal, Sumit K.; Samarabandu, Jagath; Ladak, Hanif M.; Allen, Prudence
Highlights: Analyzing Auditory Brainstem Responses (ABRs) is subjective and time-consuming for clinicians. We systematically reviewed the literature and found 34 articles that applied machine learning to the analysis of ABRs and met the inclusion criteria of the review. Three application categories of ABRs were found and the use of ML for each category has been reviewed separately. A clear and comprehensive overview is provided of the ML techniques applied to ABRs and several challenges are identified. Potential avenues are suggested to further explore ML paradigms to analyse the ABRs providing opportunities to future researchers. 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.