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Eustachian tube dysfunction (ETD) is a commonly diagnosed disorder of Eustachian tube opening and closure, which may be associated with severe symptoms and middle ear disease. Currently the diagnosis of obstructive and patulous forms of ETD is primarily based on non-specific symptoms or examination findings, rather than measurement of the underlying function of the Eustachian tube. This has proved problematic when selecting patients for treatment, and when designing trial inclusion criteria and outcomes. This study aims to determine the correlation and diagnostic value of various tests of ET opening and patient reported outcome measures (PROMs), in order to generate a recommended diagnostic pathway for ETD. Index tests included two PROMs and 14 tests of ET opening (nine for obstructive, five for patulous ETD). In the absence of an accepted reference standard two methods were adopted to establish index test accuracy: expert panel diagnosis and latent class analysis. Index test results were assessed with Pearson correlation and principle component analysis, and test accuracy was determined. Logistic regression models assessed the predictive value of grouped test results. The expert panel diagnosis and PROMs results correlated with each other, but not with ET function measured by tests of ET opening. All index tests were found to be feasible in clinic, and acceptable to patients. PROMs had very poor specificity, and no diagnostic value. Combining the results of tests of ET function appeared beneficial. The latent class model suggested tympanometry, sonotubometry and tubomanometry have the best diagnostic performance for obstructive ETD, and these are included in a proposed diagnostic pathway. ETD should be diagnosed on the basis of clinical assessment and tests of ET opening, as PROMs have no diagnostic value. Currently diagnostic uncertainty exists for some patients who appear to have intermittent ETD clinically, but have negative index test results.

Psychophysical experiments conducted remotely over the internet permit data collection from large numbers of participants but sacrifice control over sound presentation and therefore are not widely employed in hearing research. To help standardize online sound presentation, we introduce a brief psychophysical test for determining whether online experiment participants are wearing headphones. Listeners judge which of three pure tones is quietest, with one of the tones presented 180° out of phase across the stereo channels. This task is intended to be easy over headphones but difficult over loudspeakers due to phase-cancellation. We validated the test in the lab by testing listeners known to be wearing headphones or listening over loudspeakers. The screening test was effective and efficient, discriminating between the two modes of listening with a small number of trials. When run online, a bimodal distribution of scores was obtained, suggesting that some participants performed the task over loudspeakers despite instructions to use headphones. The ability to detect and screen out these participants mitigates concerns over sound quality for online experiments, a first step toward opening auditory perceptual research to the possibilities afforded by crowdsourcing.

Sound localization is essential for auditory spatial awareness. The process relies on interaural differences in timing and level, and spectral cues. This study aimed to standardize sound-localization testing conditions across facilities in Japan, analyze the impact of early reflected sounds on localization accuracy, and compare outcomes between individuals with normal hearing and those with unilateral hearing loss. This study included 77 participants with normal hearing and 45 individuals with unilateral hearing loss, at 11 facilities. Sound-localization tests were conducted using nine loudspeakers arranged in a 180° horizontal arc. The stimuli consisted of Comité Consultatif International Téléphonique et Télégraphique (CCITT) and low-pass CCITT noise bursts at randomized levels of 50, 55, and 60 dB SPL. The reflected sound measurements employed time-stretched pulses to analyze early reflections (4–7 ms). The localization accuracy was assessed using the root-mean-square error and mean deviation score. Localization performance was negatively influenced by early reflections, with reflected sound envelope area and peak values within 4–7 ms correlating significantly with reduced accuracy (r = −0.535 to −0.555). Participants with normal hearing achieved a root-mean-square error of 2.0° ± 4.8°, whereas participants with unilateral hearing loss exhibited significantly greater errors (68.4° ± 40.7°, p < .001). Asymmetries in the left–right response accuracy correlated positively with the reflected sound characteristics (r > 0.6). Noise type (normal vs. low-pass CCITT) did not significantly impact performance in either group. Early reflections significantly compromise sound-localization accuracy, particularly in smaller testing environments where reflections overlap with direct sounds. Standardized testing protocols, in which early reflections are controlled, are critical for reliable assessments. The use of sound-absorbing materials can enhance the test precision, particularly in the clinical evaluation of unilateral hearing loss. These findings emphasize the need for optimizing acoustic conditions to improve the reliability and accuracy of sound-localization testing.

The purpose of this study is to evaluate smartphone-based self-hearing test applications (apps) for accuracy in threshold assessment and validity in screening for hearing loss across frequencies and earphone transducer styles. Twenty-two adult participants (10 = normal hearing; 12 = sensorineural hearing loss; n = 44 ears) underwent conventional audiometry and performed 6 self-administered hearing tests using two iPhone-based apps (App 1 = uHear [Version 2.0.2, Unitron]; App 2 = uHearingTest [Version 1.0.3, WooFu Tech, LLC.]) each with 3 different transducers (earbud earphones, supra-aural headphones, circumaural headphones). Hearing sensitivity results using the smartphone apps across frequencies and transducers were compared with conventional audiometry. Differences in accuracy were revealed between the hearing test apps across frequencies and earphone styles. The uHear app using the iPhone standard EarPod earbud earphones was accurate to conventional thresholds (p > .002 with Bonferroni correction) at 1000, 2000, 4000, and 6000 Hz and found valid (81%-100% sensitivity, specificity, positive and negative predictive values) for screening mild or greater hearing loss (> 25 dB HL) at 500, 1000, 2000, 4000, and 6000 Hz. The uHearingTest app was accurate in threshold assessment and determined valid for screening mild or greater hearing loss (> 25 dB HL) using supra-aural headphones at 2000, 4000, and 8000 Hz. Self-hearing test apps can be accurate in hearing threshold assessment and screening for mild or greater hearing loss (> 25 dB HL) when using appropriate transducers. To ensure accuracy, manufacturers should specify earphone model instructions to users of smartphone-based self-hearing test apps.

Background Childhood hearing loss has well-known profound implications for language development, school achievement, and future employment opportunities. School-based health programs can provide hearing screening, but access to specialists for follow-up care is limited in rural areas. This is especially problematic for children in rural Alaska who experience a disproportionately high burden of preventable childhood hearing loss. The purpose of this study will be to develop and test the effectiveness and implementation of school-based specialty telehealth follow-up to improve timely access to specialty care after school hearing screening in rural Alaska. Methods This will be a hybrid type 1 effectiveness-implementation stepped wedge, cluster-randomized trial in three representative regions of Alaska. The trial will evaluate the STAR model, which consists of three core components: (1) enhanced school hearing screening, (2) school-based specialty telehealth follow-up, and (3) streamlined communication between schools, healthcare providers, and parents/caregivers. The trial will begin with a formative phase in the first 2 years, when qualitative data on community preferences and perspectives will be gathered to systematically adapt the STAR model and develop an implementation plan for participating regions. The adapted STAR model will be evaluated with a stepped wedge, cluster-randomized design in approximately 25 schools in three regions of rural Alaska. The primary effectiveness outcome will be the proportion of referrals resulting in specialty follow-up within 60 days of school hearing screening, measured using queries of electronic health records from the healthcare systems serving each region. Generalized estimating equations (GEE) will be used to model these cluster-period school-level proportions to obtain population-averaged intervention effects that are of public health relevance and therefore of interest in implementation trials. Secondary implementation outcomes will include fidelity, reach, acceptability, feasibility, and appropriateness. Sustainability of the STAR model will be evaluated through iterative meetings with state leaders and policymakers. Discussion This trial will evaluate school-based specialty telehealth follow-up in diverse regions of Alaska, addressing preventable childhood hearing loss with a model that could be translated to other rural and underserved groups to bring high-value services into rural schools and alter the paradigm of prevention nationwide. Trial registration NCT05593484. Registered on October 20, 2022.

Background As the global population ages, hearing loss becomes increasingly prevalent, and is associated with neurocognitive and psychiatric comorbidities, impacting quality of life. Early screening and timely intervention might prevent or delay cognitive decline, a gap in care that can potentially be addressed by self-administered smartphone hearing tests. Objective This study aims to evaluate the accuracy of Mimi™ (Berlin, Germany), a commercially available self-administered smartphone hearing test compared to pure tone audiogram (PTA) in terms of both hearing levels and hearing thresholds in our local geriatric population > 65 years-old. Method Fifty-two participants above 65 years of age requiring conventional audiograms were recruited from a National Referral University Hospital Otolaryngology clinic from March to June 2022. All participants were administered the conventional PTA tests in a sound-proof booth conducted by audiology technicians followed by Mimi™ Hearing Test in a quiet clinic room. Comparisons between the hearing levels of both tests were analyzed using Spearman’s rank correlation coefficient, Bland–Altman plots and Gwet’s Kappa which looked at concordance. Hearing thresholds were then analysed using the Wilcoxon signed rank (SR) test. Results Mimi™ showed strong to very strong correlation with good agreement compared to readings obtained from formal PTA. Concordance in determining hearing loss also showed substantial to almost perfect agreement at each individual frequency, with values of kappa falling between 0.735–0.857. In terms of thresholds, there were no significant differences in thresholds given by both tests except for 2.0 kHz, HFPTA and 4FPTA (p < 0.05). Conclusion Mimi™ serves as a good screening tool for detection of moderate hearing loss for early pickup and treatment except at higher frequencies. The smartphone hearing test is also less accurate in determining the extent of hearing loss and formal PTA after hearing loss is detected on screening should still be standard of care.

Background Given the critical impact of hearing impairments on both individual development and societal outcomes, particularly in the context of children’s education and health, this study conducted a retrospective analysis of hearing screening test results among first-grade students. Methods The study employed a retrospective cross-sectional design, analyzing the hearing screening test results of first-grade students (aged 6–7 years) from the Umraniye district during the 2022–2023 academic year. Data from 10,638 children with complete and comprehensive records were included in the analysis. Hearing impairment was measured non-invasively via pure tone audiometry at four frequencies (500, 1000, 2000, and 4000 Hz) at 20 dB over approximately five minutes—with failure defined as no response at any frequency in one or both ears, prompting a repeat screening within 48 h and, if still failed, referral for comprehensive evaluation. Chi-Square test (or Fisher Exact test where appropriate) was used for comparison of categorized data. A binary logistic regression model was applied to assess the effect of the independent variables on hearing screening outcomes ( passed in both ears vs. failed in one or both ears ), with adjusted odds ratios (ORs) and 95% confidence intervals (CIs) reported. p  < 0.05 was considered statistically significant. Results The percentage of children who passed the hearing screening test in both ears was 72.8% ( n  = 7,747). Potential risk factors associated with failed hearing screening in one or both ears were examined. Children who failed the screening test exhibited significantly higher rates of learning difficulties, difficulty hearing when called from another room, a history of ototoxic drug use, and a history of postnatal infections resulting in sensorineural hearing loss ( p  < 0.001, p  < 0.001, p  = 0.003, and p  < 0.001, respectively). Conclusion Our findings underscore the importance of early identification and intervention for hearing loss. Future research should focus on refining surveillance and screening protocols to better detect and manage hearing impairments in at-risk pediatric populations, ensuring timely diagnosis and appropriate intervention strategies.

Traditional diagnostic methods of hearing assessment, such as pure tone audiometry, may not be equally accessible to everyone due to geographical or mobility limitations. Utilizing a mobile application (app) for self-assessment of hearing is a promising alternative. However, the effectiveness of apps, as well as their usability across different age groups, remains largely unexplored. The objective of the present study was to assess, across different age groups, the usability of the \"Hearing Test\" app which allows self-testing of hearing on a mobile phone. The study was conducted on 77 participants from three age groups (16-39 years, 40-59 years, 60 years and older) who self-tested their hearing thresholds using the mobile app and who later underwent pure tone audiometry with an audiologist. The usability of the app was evaluated using a questionnaire based on the Mobile App Rating Scale (MARS), which was complemented by participant observation and interview. The app generally yielded results comparable to pure tone audiometry. However, older age groups tended to report higher levels of difficulty across several usability dimensions. Specifically, the oldest group rated the app lower in terms of functionality (M = 2.30; SD = 1.27) and engagement-customization (M = 2.11; SD = 1.28). For the oldest participants, the greatest difficulties related to installation (48%), and interpretation of results (26%). None of the participants aged 60 or older were able to complete the test independently, in contrast to 67% of the youngest participants and 28% of the middle-aged who did not require assistance. All age groups expressed a preference for a conventional hearing test over an app-based assessment, although the youngest group showed the greatest openness to using mobile apps. The \"Hearing Test\" app has demonstrated its potential as a tool for initial hearing assessment, particularly among younger users. However, older individuals often encounter difficulties with installation, interpretation of results, and overall usability. Adapting the interface to meet the specific needs of older users, including user-friendly tutorials and clear presentation of results, is crucial for enhancing its usability.

This study aims to explore the feasibility and accuracy of deep learning-based pediatric behavioral audiometry. The research provides a dedicated pediatric posture detection dataset, which contains a large number of video clips from children’s behavioral hearing tests, encompassing various typical hearing test actions. A detection platform based on this dataset is also constructed, named intelligent diagnostic model of pediatric hearing based on o ptimized transformer (DoT); further, an estimation model of patient skeletal keypoints based on o ptimized transformer (POTR) was proposed to estimate human skeleton points. Based on this, the DoT approach was handled to perform posture recognition on videos of children undergoing behavioral hearing tests, thus enabling an automated hearing testing process. Through this platform, children’s movements can be monitored and analyzed in real-time, allowing for the assessment of their hearing levels. Moreover, the study establishes decision rules based on specific actions, combining professional knowledge and experience in audiology to evaluate children’s hearing levels based on their movement status. Firstly, we gathered image and video data related to posture in the process of conditioned play audiometry to test the hearing of 120 children aged 2.5 to 6 years old. Next, we built and optimized a deep learning model suitable for pediatric posture recognition. Finally, in the deployment and application phase, we deployed the trained pediatric posture recognition model into real-world application environments. We found that for children aged 2.5 - 4 years, the sensitivity of artificial behavior audiometry (0.900) was not as high as that of AI behavior audiometry (0.929), but the specificity of artificial behavior audiometry (0.824) and Area Under Curve (AUC) (0.901) was higher than that of AI behavior audiometry. For children aged 4–6 years, the sensitivity (0.943), specificity (0.947), and AUC (0.924) of artificial behavioral audiometry were higher than those of AI behavioral audiometry. The application of these rules facilitates objective assessment and diagnosis of children’s hearing, providing essential foundations for early screening and treatment of children with hearing disorders. Trial Registration: Chinese Clinical Trial Registry: Registration number ChiCTR2100050416.

Understanding speech in noisy environments is a primary challenge for individuals with hearing loss, affecting daily communication and quality of life. Traditional speech-in-noise tests are essential for screening and diagnosing hearing loss but are resource-intensive to develop, making them less accessible in low and middle-income countries. This study introduces an artificial intelligence-based approach to automate the development of these tests. By leveraging text-to-speech and automatic speech recognition (ASR) technologies, the cost, time, and resources required for high-quality speech-in-noise testing could be reduced. The procedure, named “Aladdin” (Automatic LAnguage-independent Development of the digits-in-noise test), creates digits-in-noise (DIN) hearing tests through synthetic speech material and uses ASR-based level corrections to perceptually equalize the digits. Traditional DIN tests were compared with newly developed Dutch and English Aladdin tests in listeners with normal hearing and hearing loss. Aladdin tests showed 84% specificity and 100% sensitivity, similar to the reference DIN tests (87% and 100%). Aladdin provides a universal guideline for developing DIN tests across languages, addressing the challenge of comparing test results across variants. Aladdin’s approach represents a significant advancement in test development and offers an efficient enhancement to global screening and treatment for hearing loss.