Heisey Katherine L, Walker Alexandra M, Xie Kevin, Abrams Jenna M, Barbour Dennis L
Department of Biomedical Engineering, Laboratory of Sensory Neuroscience and Neuroengineering, Washington University in St. Louis, St. Louis, Missouri, USA.
Program in Audiology and Communication Sciences, Department of Otolaryngology, Washington University School of Medicine, St. Louis, Missouri, USA.
Ear Hear. 2020 Nov/Dec;41(6):1692-1702. doi: 10.1097/AUD.0000000000000891.
When one ear of an individual can hear significantly better than the other ear, evaluating the worse ear with loud probe tones may require delivering masking noise to the better ear to prevent the probe tones from inadvertently being heard by the better ear. Current masking protocols are confusing, laborious, and time consuming. Adding a standardized masking protocol to an active machine learning audiogram procedure could potentially alleviate all of these drawbacks by dynamically adapting the masking as needed for each individual. The goal of this study is to determine the accuracy and efficiency of automated machine learning masking for obtaining true hearing thresholds.
Dynamically masked automated audiograms were collected for 29 participants between the ages of 21 and 83 (mean 43, SD 20) with a wide range of hearing abilities. Normal-hearing listeners were given unmasked and masked machine learning audiogram tests. Listeners with hearing loss were given a standard audiogram test by an audiologist, with masking stimuli added as clinically determined, followed by a masked machine learning audiogram test. The hearing thresholds estimated for each pair of techniques were compared at standard audiogram frequencies (i.e., 0.25, 0.5, 1, 2, 4, 8 kHz).
Masked and unmasked machine learning audiogram threshold estimates matched each other well in normal-hearing listeners, with a mean absolute difference between threshold estimates of 3.4 dB. Masked machine learning audiogram thresholds also matched well the thresholds determined by a conventional masking procedure, with a mean absolute difference between threshold estimates for listeners with low asymmetry and high asymmetry between the ears, respectively, of 4.9 and 2.6 dB. Notably, out of 6200 masked machine learning audiogram tone deliveries for this study, no instances of tones detected by the nontest ear were documented. The machine learning methods were also generally faster than the manual methods, and for some listeners, substantially so.
Dynamically masked audiograms achieve accurate true threshold estimates and reduce test time compared with current clinical masking procedures. Dynamic masking is a compelling alternative to the methods currently used to evaluate individuals with highly asymmetric hearing, yet can also be used effectively and efficiently for anyone.
当一个人的一只耳朵听力明显优于另一只耳朵时，用高声强探测音评估听力较差的耳朵可能需要向听力较好的耳朵施加掩蔽噪声，以防止探测音被听力较好的耳朵意外听到。当前的掩蔽方案令人困惑、费力且耗时。在主动机器学习听力图程序中添加标准化的掩蔽方案，有可能通过根据每个人的需要动态调整掩蔽来缓解所有这些缺点。本研究的目的是确定自动机器学习掩蔽在获取真实听力阈值方面的准确性和效率。
为29名年龄在21至83岁（平均43岁，标准差20岁）、听力能力范围广泛的参与者收集了动态掩蔽自动听力图。听力正常的受试者接受了未掩蔽和掩蔽的机器学习听力图测试。听力损失的受试者由听力学家进行标准听力图测试，并根据临床判断添加掩蔽刺激，随后进行掩蔽机器学习听力图测试。在标准听力图频率（即0.25、0.5、1、2、4、8kHz）下比较了每种技术对估计的听力阈值。
在听力正常的受试者中，掩蔽和未掩蔽的机器学习听力图阈值估计相互匹配良好，阈值估计的平均绝对差异为3.4dB。掩蔽机器学习听力图阈值也与传统掩蔽程序确定的阈值匹配良好，双耳低不对称和高不对称受试者的阈值估计平均绝对差异分别为4.9dB和2.6dB。值得注意的是，在本研究的6200次掩蔽机器学习听力图音调发放中，未记录到非测试耳检测到音调的情况。机器学习方法通常也比手动方法更快，对一些受试者来说，快得多。
与当前的临床掩蔽程序相比，动态掩蔽听力图能实现准确的真实阈值估计并减少测试时间。动态掩蔽是目前用于评估听力高度不对称个体的方法的有力替代方案，并且对任何人都可以有效且高效地使用。
