Abstract
We propose a machine learning (ML)-based model for predicting cochlear dead regions (DRs) in patients with hearing loss of various etiologies. Five hundred and fifty-five ears from 380 patients (3,770 test samples) diagnosed with sensorineural hearing loss (SNHL) were analyzed. A threshold-equalizing noise (TEN) test was applied to detect the presence of DRs. Data were collected on sex, age, side of the affected ear, hearing loss etiology, word recognition scores (WRS), and pure-tone thresholds at each frequency. According to the cause of hearing loss as diagnosed by the physician, we categorized the patients into six groups: 1) SNHL with unknown etiology; 2) sudden sensorineural hearing loss (SSNHL); 3) vestibular schwannoma (VS); 4) Meniere's disease (MD); 5) noise-induced hearing loss (NIHL); or 6) presbycusis or age-related hearing loss (ARHL). To develop a predictive model, we performed recursive partitioning and regression for classification, logistic regression, and random forest. The overall prevalence of one or more DRs in test ears was 20.36% (113 ears). Among the 3,770 test samples, the overall frequency-specific prevalence of DR was 6.7%. WRS, pure-tone thresholds at each frequency, disease type (VS or MD), and frequency information were useful for predicting DRs. Sex and age were not associated with detecting DRs. Based on these results, we suggest possible predictive factors for determining the presence of DRs. To improve the predictive power of the model, a more flexible model or more clinical features, such as the duration of hearing loss or risk factors for developing DRs, may be needed.
Highlights
A cochlear dead region (DR) is defined as a region in the cochlea where the inner hair cells (IHCs) and/or neurons lose normal function at a related frequency
The prevalence of DRs was significantly higher in the VS group, compared to the sensorineural hearing loss (SNHL) with unknown etiology group (p < 0.001, Chi-square test)
Previous studies have revealed reliable indicators of DRs based on detection by thresholdequalizing noise (TEN) (HL) tests [2, 11, 12], the prevalence and possible indicators of DRs differ according to the study population [2, 12, 25, 26]
Summary
A cochlear dead region (DR) is defined as a region in the cochlea where the inner hair cells (IHCs) and/or neurons lose normal function at a related frequency. A previous study had reported that DRs are associated with potentially poor hearing thresholds on follow-up audiograms in patients with sudden sensorineural hearing loss (SSNHL) [1]. The threshold-equalizing noise test proposed by Moore et al [7] is designed to detect the presence of a cochlear dead region (DR) in a clinical setting. When the pure-tone signal frequency falls in a DR, the signal will only be detected when it produces sufficient basilar membrane vibration in a remote region of the cochlea where there are surviving IHCs and neurons. A later version was designed to provide approximately the same masked pure-tone thresholds in dB HL for wide frequencies (500–4000 Hz) and is referred to as the TEN (HL) test [7]
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