Abstract
Background/Objectives: Cochlear implantation is an advantageous procedure for individuals with severe to profound hearing loss in many aspects related to auditory performance, social communication and quality of life. As machine learning applications have been used in the field of Otorhinolaryngology and Audiology in recent years, signal processing, speech perception and personalised optimisation of cochlear implantation are discussed. Methods: A comprehensive literature review was conducted in accordance with the PRISMA guidelines. PubMed, Scopus, Web of Science, Google Scholar and IEEE databases were searched for studies published between 2010 and 2025. We analyzed 59 articles that met the inclusion criteria. Rayyan AI software was used to classify the studies so that the risk of bias was reduced. Study design, machine learning algorithms, and audiological measurements were evaluated in the data analysis. Results: Machine learning applications were classified as preoperative evaluation, speech perception, and speech understanding in noise and other studies. The success rates of the articles are presented together with the number of articles changing over the years. It was observed that Random Forest, Decision Trees (96%), Bayesian Linear Regression (96.2%) and Extreme machine learning (99%) algorithms reached high accuracy rates. Conclusions: In cochlear implantation applications in the field of audiology, it has been observed that studies have been carried out with a variable number of people and data sets in different subfields. In machine learning applications, it is seen that a high amount of data, data diversity and long training times contribute to achieving high performance. However, more research is needed on deep learning applications in complex problems such as comprehension in noise that require time series processing. Funding and other resources: This study was not funded by any institution or organization. No registration was performed for this study.