Jafari Zahra, Harari Ryan E, Hole Glenn, Kolb Bryan E, Mohajerani Majid H
School of Communication Sciences and Disorders (SCSD), Dalhousie University, Halifax, Nova Scotia, Canada.
Department of Psychology and Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada.
Ear Hear. 2025;46(5):1305-1316. doi: 10.1097/AUD.0000000000001670. Epub 2025 May 6.
Despite the extensive use of machine learning (ML) models in health sciences for outcome prediction and condition classification, their application in differentiating various types of auditory disorders remains limited. This study aimed to address this gap by evaluating the efficacy of five ML models in distinguishing (a) individuals with tinnitus from those without tinnitus and (b) noise-induced hearing loss (NIHL) from age-related hearing loss (ARHL).
We used data from a cross-sectional study of the Canadian population, which included audiologic and demographic information from 928 adults aged 30 to 100 years, diagnosed with either ARHL or NIHL due to long-term occupational noise exposure. The ML models applied in this study were artificial neural networks (ANNs), K-nearest neighbors, logistic regression, random forest (RF), and support vector machines.
The study revealed that tinnitus prevalence was over twice as high in the NIHL group compared with the ARHL group, with a frequency of 27.85% versus 8.85% in constant tinnitus and 18.55% versus 10.86% in intermittent tinnitus. In pattern recognition, significantly greater hearing loss was found at medium- and high-band frequencies in NIHL versus ARHL. In both NIHL and ARHL, individuals with tinnitus showed better pure-tone sensitivity than those without tinnitus. Among the ML models, ANN achieved the highest overall accuracy (70%), precision (60%), and F1-score (87%) for predicting tinnitus, with an area under the curve of 0.71. RF outperformed other models in differentiating NIHL from ARHL, with the highest precision (79% for NIHL, 85% for ARHL), recall (85% for NIHL), F1-score (81% for NIHL), and area under the curve (0.90).
Our findings highlight the application of ML models, particularly ANN and RF, in advancing diagnostic precision for tinnitus and NIHL, potentially providing a framework for integrating ML techniques into clinical audiology for improved diagnostic precision. Future research is suggested to expand datasets to include diverse populations and integrate longitudinal data.
尽管机器学习（ML）模型在健康科学领域被广泛用于结果预测和疾病分类，但其在区分各种类型听觉障碍方面的应用仍然有限。本研究旨在通过评估五种ML模型在区分（a）耳鸣患者与非耳鸣患者以及（b）噪声性听力损失（NIHL）与年龄相关性听力损失（ARHL）方面的功效来填补这一空白。
我们使用了来自加拿大人群横断面研究的数据，其中包括928名年龄在30至100岁之间的成年人的听力和人口统计学信息，这些人因长期职业噪声暴露而被诊断为ARHL或NIHL。本研究中应用的ML模型有人工神经网络（ANNs）、K近邻、逻辑回归、随机森林（RF）和支持向量机。
研究表明，NIHL组的耳鸣患病率是ARHL组的两倍多，持续性耳鸣的患病率分别为27.85%和8.85%，间歇性耳鸣的患病率分别为18.55%和10.86%。在模式识别方面，与ARHL相比，NIHL在中高频段的听力损失明显更大。在NIHL和ARHL中，有耳鸣的个体比没有耳鸣的个体表现出更好的纯音敏感度。在ML模型中，ANN在预测耳鸣方面达到了最高的总体准确率（70%）、精确率（60%）和F1分数（87%），曲线下面积为 = 0.71。在区分NIHL与ARHL方面，RF的表现优于其他模型，具有最高的精确率（NIHL为79%，ARHL为85%）、召回率（NIHL为85%）、F1分数（NIHL为81%）和曲线下面积（0.90）。
我们的研究结果突出了ML模型，特别是ANN和RF，在提高耳鸣和NIHL诊断准确性方面的应用，有可能为将ML技术整合到临床听力学中以提高诊断准确性提供一个框架。建议未来的研究扩大数据集以纳入不同人群并整合纵向数据。
