A Review On Machine Learning Assessment Of Hearing Loss

Abstract

This study's goal is to give a summary of digital methods for automated hearing loss estimation utilizing pure tone audiometry, with a particular emphasis on issues of accuracy, dependability, and timeliness. This study aims to provide a comprehensive analysis of the
2013 systematic review and make it useful to a larger audience. By 2050, it is expected that 25% of the world's population would have hearing impairment, up from the current 20% prevalence. As therapy depends on a correct diagnosis of hearing loss, this first step is out of reach for more than 80% of those who are affected. For the purpose of evaluating the commonalities between the various research, significant information regarding the purpose and specifics of each report was gathered. Their reports from numerous studies conducted between 2012 and June 2021 were included. Numerous distinctive automated methods were found from this selection. In comparison to conventional range-obtaining methods, machine learning algorithms require fewer trials, and individualized digital tools make assessment more affordable and available. Using digital approaches for quality monitoring, such as noise monitoring and result inconclusiveness detection, can improve validity. A growing variety of automated methods have demonstrated accuracy, dependability, and time effectiveness comparable to manual hearing evaluations over the last ten years. Beyond human audiometry, new developments—including methods for machine learning—offer features, cost-effectiveness, and variety. When carried out according to predetermined guidelines, automated evaluation using digital technologies can facilitate work-shifting, self-care, telehealth, and clinical treatment options.