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Basic Concepts of Clinical Electrophysiology in Audiology is a revolutionary textbook, combining the research and expertise of both distinguished experts and up-and-coming voices in the field. By taking a multidisciplinary approach to the subject, the editors of this graduate-level text break down all aspects of electrophysiology to make it accessible to audiology students. In addition to defining the basics of the tools of the trade and their routine uses, the authors also provide ample presentations of new approaches currently undergoing continuing research and development. The goal of this textbook is to give developing audiologists a broad and solid basis of understanding of the methods in common or promising practice.

Hearing loss affects 1 in 5 people worldwide and is estimated to affect 1 in 4 by 2050. Treatment relies on the accurate diagnosis of hearing loss; however, this first step is out of reach for >80% of those affected. Increasingly automated approaches are being developed for self-administered digital hearing assessments without the direct involvement of professionals. This study aims to provide an overview of digital approaches in automated and machine learning assessments of hearing using pure-tone audiometry and to focus on the aspects related to accuracy, reliability, and time efficiency. This review is an extension of a 2013 systematic review. A search across the electronic databases of PubMed, IEEE, and Web of Science was conducted to identify relevant reports from the peer-reviewed literature. Key information about each report's scope and details was collected to assess the commonalities among the approaches. A total of 56 reports from 2012 to June 2021 were included. From this selection, 27 unique automated approaches were identified. Machine learning approaches require fewer trials than conventional threshold-seeking approaches, and personal digital devices make assessments more affordable and accessible. Validity can be enhanced using digital technologies for quality surveillance, including noise monitoring and detecting inconclusive results. In the past 10 years, an increasing number of automated approaches have reported similar accuracy, reliability, and time efficiency as manual hearing assessments. New developments, including machine learning approaches, offer features, versatility, and cost-effectiveness beyond manual audiometry. Used within identified limitations, automated assessments using digital devices can support task-shifting, self-care, telehealth, and clinical care pathways.

Hearing loss is a major public health problem. In 2050, it could affect 2.5 billion people. It has therefore become necessary to prevent and diagnose them as early and as widely as possible. However, the costs of clinical equipment dedicated to the functional exploration of hearing remain high and hamper their distribution, while the technologies used are relatively basic. For example, the gold-standard pure-tone audiometry (PTA) essentially consists of emitting pure sounds. In addition, clinical audiometers are generally limited to PTA or few audiological tests, while hearing loss induce multiple functional deficits. Here, we present the Aupiometer, a low-cost audiometer implemented on a modular open-source system based on Raspberry Pi, and which integrates the entire technical framework necessary to carry out audiological measurements. Several hearing tests are already implemented (e.g. PTA, speech audiometry, questionnaires), while the clinical validity of the Aupiometer was verified on a panel of participants (N = 16) for an automated test of standard and extended high-frequency PTA, from 0.125 to 16 kHz, in comparison with a clinical audiometer. For this comparison between the two devices and over this wide frequency range, the difference is evaluated as less than ±10 dB for a 90% confidence interval, of the same order of magnitude as on test-retest differences on a single device. The interest of this device also extends to academic research as it should encourage the prototyping of innovative hearing tests by the community, in order to better understand the diversity of hearing problems in the population.

ObjectivesHearing loss (HL) affects 20% of the world’s population, with shortages of audiologists and audiometric sound booths unable to meet demand for hearing care services. We aimed to assess the accuracy of tablet-based audiometry (TA) to screen for HL at standard (0.25–8 kHz) and extended high frequencies (>8 kHz).DesignDiagnostic accuracy study.SettingTwo secondary care audiology and ear, nose and throat outpatient clinics in the UK between April 2022 and September 2023.ParticipantsAdults aged≥16 years undergoing sound booth audiometry (SBA).InterventionsTA, hearing-related questionnaires and patient usability questionnaires.Outcome measuresSensitivity, specificity and accuracy of TA compared with SBA for detecting HL. Patient usability assessment of TA and SBA.Results129 patients were enrolled with 127 patients (254 ears) included in the final analysis. Median age was 43 years (IQR 33–56), 55% (70/127) were women. 76% (96/127) and 68% (86/127) of patients had HL defined by British Society of Audiology (BSA) and American Speech–Language–Hearing Association (ASHA) criteria. Age was significantly associated with HL (p<0.0001); however, hearing-related questionnaire scores were not significantly different between those with or without HL. There was no significant difference in detecting HL between TA and SBA using either BSA or ASHA criteria at each frequency. Overall, 92% (1612/1751) of TA results were within 10 dB agreement with SBA results. Sensitivity and specificity of TA for detecting HL were 77–100% and >85%, respectively, between 0.25 and 12.5 kHz. In terms of patient usability, TA showed significantly higher scores in attractiveness (p<0.0001), novelty (p<0.0001), efficiency (p=0.0003), stimulation (p=0.003) and perspicuity (p=0.02).ConclusionsTA demonstrated good sensitivity with high specificity for detecting HL at frequencies 0.25–12.5 kHz and would be an acceptable accurate alternative to SBA. This would increase the accessibility of HL screening and has the potential to be used as a diagnostic test in those without tinnitus where resources are limited.Trial registration numberNCT05847556.

Brain stem evoked response audiometry (BERA) is a useful objective assessement of hearing. Major advantage of this procedure is its ability to test even infants in whom conventional audiometry may not be useful. This investigation can be used as a screening test for deafness in high risk infants. Early diagnosis and rehabilitation will reduce disability in these children. This article attempts to review the published literature on this subject.

Rachel A. McArdle Bay Pines VA Healthcare System, Bay Pines, FL, and University of South Florida, Tampa Sherri L. Smith James H. Quillen VA Medical Center and East Tennessee State University Contact author: Richard H. Wilson, James H. Quillen VA Medical Center, Audiology (126), Mountain Home, TN 37684. E-mail: richard.wilson2{at}va.gov . Purpose: The purpose of this study was to examine in listeners with normal hearing and listeners with sensorineural hearing loss the within- and between-group differences obtained with 4 commonly available speech-in-noise protocols. Method: Recognition performances by 24 listeners with normal hearing and 72 listeners with sensorineural hearing loss were compared for 4 speech-in-noise protocols that varied with respect to the amount of contextual cues conveyed in the target signal. The protocols studied included the Bamford-Kowal-Bench Speech-in-Noise Test (BKB-SIN; Etym tic Research, 2005; J. Bench, A. Kowal, & J. Bamford, 1979; P. Niquette et al., 2003), the Quick Speech-in-Noise Test (QuickSIN; M. C. Killion, P. A. Niquette, G. I. Gudmundsen, L. J. Revit, & S. Banerjee, 2004), and the Words-in-Noise test (WIN; R. H. Wilson, 2003; R. H. Wilson & C. A. Burks, 2005), each of which used multitalker babble and a modified method of constants, as well as the Hearing in Noise Test (HINT; M. Nilsson, S. Soli, & J. Sullivan, 1994), which used speech-spectrum noise and an adaptive psychophysical procedure. Results: The 50% points for the listeners with normal hearing were in the 1- to 4-dB signal-to-babble ratio (S/B) range and for the listeners with hearing loss in the 5- to 14-dB S/B range. Separation between groups was least with the BKB-SIN and HINT (4–6 dB) and most with the QuickSIN and WIN (8–10 dB). Conclusion: The QuickSIN and WIN materials are more sensitive measures of recognition performance in background noise than are the BKB-SIN and HINT materials. KEY WORDS: auditory perception, hearing loss, speech perception, word recognition in multitalker babble CiteULike     Connotea     Del.icio.us     Digg     Facebook     Reddit     Technorati     Twitter     What's this?

Young healthy adults can hear tones up to at least 20 kHz. However, clinical audiometry, by which hearing loss is diagnosed, is limited at high frequencies to 8 kHz. Evidence suggests there is salient information at extended high frequencies (EHFs; 8 to 20 kHz) that may influence speech intelligibility, but whether that information is used in challenging listening conditions remains unknown. Difficulty understanding speech in noisy environments is the most common concern people have about their hearing and usually the first sign of age-related hearing loss. Digits-in-noise (DIN), a widely used test of speech-in-noise perception, can be sensitized for detection of high-frequency hearing loss by low-pass filtering the broadband masking noise. Here, we used standard and EHF audiometry, self-report, and successively higher cutoff frequency filters (2 to 8 kHz) in a DIN test to investigate contributions of higher-frequency hearing to speech-in-noise perception. Three surprising results were found. First, 74 of 116 “normally hearing,” mostly younger adults had some hearing loss at frequencies above 8 kHz. Early EHF hearing loss may thus be an easily measured, preventive warning to protect hearing. Second, EHF hearing loss correlated with self-reported difficulty hearing in noise. Finally, even with the broadest filtered noise (≤8 kHz), DIN hearing thresholds were significantly better (P < 0.0001) than those using broadband noise. Sound energy above 8 kHz thus contributes to speech perception in noise. People with “normal hearing” frequently report difficulty hearing in challenging environments. Our results suggest that one contribution to this difficulty is EHF hearing loss.

Background We designed and validated a speech recognition test for Persian-speaking children aged 36–71 months. Methods This project was a cross-sectional and methodological study. Different steps of test development, encompassing item generation, content validity, construct validity, and reliability, were used to develop the scale. Results The percentages of agreement among the experts’ answers concerning test characteristics were all greater than 88%. After this phase, 20 items were removed. In the second phase of content validity, 18 additional pictures were suggested to be removed by the experts. Finally, a test with 162 pictures was developed among which 120 corresponding words were presented orally (the remaining pictures were distractors). The Persian speech recognition test revealed an age difference in speech recognition for both ears ( p-value < 0.001 , df = 5 ). There was no significant association between sex and total score on the Persian speech recognition test for right ear (U = 3063, p- value = 0.092) and left ear (U = 3009, p-value = 0.063 ). Test-retest values were excellent for both ears (right ear: r  = 0.97, left ear: r  = 0.98), p-value < 0.001 ). Conclusions Given the findings in typically developing (TD) children, the Persian speech recognition test is valid and reliable. However, future studies are highly recommended to apply this test in hearing-impaired children.

Background There is evidence to suggest that rates of hearing loss are increasing more rapidly than the capacity of traditional audiometry resources for screening. A novel innovation in tablet, self-administered portable audiometry has been proposed as a solution to this discordance. The primary objective of this study was to validate a tablet audiometer with adult patients in a clinical setting. Secondarily, word recognition with a tablet audiometer was compared against conventional audiometry. Methods Three distinct prospective adult cohorts underwent testing. In group 1 and group 2 testing with the automated tablet audiometer was compared to standard sound booth audiometry. In Group 1, participants’ pure tone thresholds were measured with an automated tablet audiometer in a quiet clinic exam room. In Group 2, participants completed monosyllabic word recognition testing using the NU-6 word lists. In Group 3, internal reliability was tested by having participants perform two automated tablet audiometric evaluation in sequence. Results Group 1 included 40 patients mean age was 54.7 ± 18.4 years old and 60% female; Group 2 included 44 participants mean age was 55.2 ± 14.8 years old and 68.2% female; Group 3 included 40 participants with mean age of 39.4  +  15.9 years old and 60.5% female. In Group 1, compared to standard audiometry, 95.7% (95% CI: 92.6–98.9%) of thresholds were within 10 dB. In Group 2, comparing word recognition results, 96.2% (95% CI: 89.5–98.7%) were clinically equivalent and within a critical difference range. In Group 3, One-way Intraclass Correlation for agreement for the both left- and right-ear pure tone average was 0.98. The mean difference between repeat assessments was 0 (SD = 2.1) in the left ear, and 0.1 (SD = 1.1) in the right ear. Conclusion Puretone audiometry and word recognition testing appears valid when performed by non-healthcare experts using a tablet audiometer outside a sound booth in a quiet environment. Trial registration ClinicalTrials.gov Identifier: NCT02761798. Registered April, 2016 < https://clinicaltrials.gov/ct2/show/NCT02761798>

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.