Wallaert Nicolas, Perry Antoine, Quarino Sandra, Jean Hadrien, Creff Gwenaelle, Godey Benoit, Paraouty Nihaad
Department of Otorhinolaryngology-Head and Neck Surgery Rennes University Hospital Rennes France.
R&D Department My Medical Assistant SAS Reims France.
World J Otorhinolaryngol Head Neck Surg. 2024 Sep 12;11(2):173-188. doi: 10.1002/wjo2.208. eCollection 2025 Jun.
Automated air-conduction pure-tone audiograms through Bayesian estimation and machine learning (ML) classification have recently been proposed in the literature. Although such ML-based audiometry approaches represent a significant addition to the field, they remain unsuited for daily clinical settings, in particular for listeners with asymmetric or conductive hearing loss, severe hearing loss, or cochlear dead zones. The goal here is to expand on previously proposed ML approaches and assess the performance of this improved ML audiometry for a large sample of listeners with a wide range of hearing status.
First, we describe the changes made to the ML method through the addition of: (1) safety limits to test listeners with a wide range of hearing status, (2) transient responses to cater for cochlear dead zones or nonmeasurable thresholds, and importantly, (3) automated contralateral masking to test listeners with asymmetric or conductive hearing loss. Next, we compared the performance of this improved ML audiometry with conventional and manual audiometry in a large cohort ( = 109 subjects) of both normal-hearing and hearing-impaired listeners.
Our results showed that for all audiometric frequencies tested, no significant difference was found between hearing thresholds obtained using manual audiometry on a clinical audiometer as compared to both the manual and automated improved ML methods. Furthermore, the test-retest difference was not significant with the automated improved ML method for each audiometric frequency tested. Finally, when examining cross-clinic reliability measures, significant differences were found for most audiometric frequencies tested.
Together, our results validate the use of this improved ML-based method in adult clinical tests for air-conduction audiometry.
文献中最近提出了通过贝叶斯估计和机器学习（ML）分类实现的自动气导纯音听力图。尽管这种基于ML的听力测定方法是该领域的一项重要补充，但它们仍不适用于日常临床环境，特别是对于患有不对称或传导性听力损失、重度听力损失或耳蜗死区的听众。此处的目标是扩展先前提出的ML方法，并评估这种改进的ML听力测定法在具有广泛听力状况的大量听众样本中的性能。
首先，我们描述了通过添加以下内容对ML方法所做的更改：（1）安全限制，以测试具有广泛听力状况的听众；（2）瞬态响应，以适应耳蜗死区或不可测量的阈值，重要的是，（3）自动对侧掩蔽，以测试患有不对称或传导性听力损失的听众。接下来，我们在一个由正常听力和听力受损听众组成的大型队列（n = 109名受试者）中，将这种改进的ML听力测定法的性能与传统和手动听力测定法进行了比较。
我们的结果表明，对于所有测试的听力频率，在临床听力计上使用手动听力测定法获得的听力阈值与手动和自动改进的ML方法相比，没有发现显著差异。此外，对于每个测试的听力频率，自动改进的ML方法的重测差异不显著。最后，在检查跨诊所可靠性指标时，发现大多数测试的听力频率存在显著差异。
总之，我们的结果验证了这种改进的基于ML的方法在成人气导听力测定临床测试中的应用。
