Demirtaş Yılmaz Beyza
Department of Audiology, Faculty of Health Sciences, Erciyes University, Kayseri 38039, Turkey.
Audiol Res. 2025 May 8;15(3):56. doi: 10.3390/audiolres15030056.
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. 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. 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. 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. This study was not funded by any institution or organization. No registration was performed for this study.
对于重度至极重度听力损失患者而言，人工耳蜗植入在听觉表现、社交沟通和生活质量等诸多方面都是一种具有优势的治疗手段。近年来，随着机器学习应用已在耳鼻咽喉科和听力学领域得到应用，本文对人工耳蜗植入的信号处理、言语感知和个性化优化进行了探讨。按照PRISMA指南进行了全面的文献综述。在PubMed、Scopus、科学网、谷歌学术和IEEE数据库中检索了2010年至2025年发表的研究。我们分析了59篇符合纳入标准的文章。使用Rayyan人工智能软件对研究进行分类，以降低偏倚风险。在数据分析中评估了研究设计、机器学习算法和听力学测量指标。机器学习应用被分类为术前评估、言语感知、噪声中的言语理解及其他研究。文章的成功率与多年来文章数量的变化情况一同呈现。观察到随机森林、决策树（96%）、贝叶斯线性回归（96.2%）和极限机器学习（99%）算法达到了较高的准确率。在听力学领域的人工耳蜗植入应用中，观察到不同子领域针对不同数量的人群和数据集开展了研究。在机器学习应用中，可以看出大量的数据、数据多样性和较长的训练时间有助于实现高性能。然而，对于诸如需要时间序列处理的噪声中的言语理解等复杂问题的深度学习应用，还需要更多的研究。本研究未获得任何机构或组织的资助。本研究未进行注册。
