The NTID speech recognition test: NSRT®

Phoneme Categorization in Prelingually Deaf Adult Cochlear Implant Users

PURPOSE

Phoneme categorization (PC) for voice onset time and second formant transition was studied in adult cochlear implant (CI) users with early-onset deafness and hearing controls.

METHOD

Identification and discrimination tasks were administered to 30 participants implanted before 4 years of age, 21 participants implanted after 7 years of age, and 21 hearing individuals.

RESULTS

Distinctive identification and discrimination functions confirmed PC within all groups. Compared to hearing participants, the CI groups generally displayed longer/higher category boundaries, shallower identification function slopes, reduced identification consistency, and reduced discrimination performance. A principal component analysis revealed that identification consistency, discrimination accuracy, and identification function slope, but not boundary location, loaded on a single factor, reflecting general PC performance. Earlier implantation was associated with better PC performance within the early CI group, but not the late CI group. Within the early CI group, earlier implantation age but not PC performance was associated with better speech recognition. Conversely, within the late CI group, better PC performance but not earlier implantation age was associated with better speech recognition.

CONCLUSIONS

Results suggest that implantation timing within the sensitive period before 4 years of age partly determines the level of PC performance. They also suggest that early implantation may promote development of higher level processes that can compensate for relatively poor PC performance, as can occur in challenging listening conditions.

The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

Toward a New Evidence-Based Fitting Paradigm for Over-the-Counter Hearing Aids

Purpose This dual-aim study was intended to develop and validate a new fitting paradigm for over-the-counter (OTC) hearing aids. Aim 1 was undertaken to create a limited set of evidence-based preconfigured gain-frequency responses ("presets") for use in OTC devices. Aim 2 tested the efficacy of the presets relative to best-practice verification and determined the best method(s) for older adults to self-select amplification from the set. Method In Aim 1, audiometry data from 267 older adults with mild-to-moderate sensorineural hearing loss were obtained from the National Health and Nutrition Examination Survey database. Using these data and the National Acoustic Laboratories-Non-Linear 2 (NAL-NL2) prescriptive formula, a set of four presets were created that can fit a large percentage of older Americans with presbycusis. In Aim 2, 37 older adults with hearing loss used five methods to select presets. The selection methods were select-by-audiogram, select-by-self-test, select-by-trying, select-by-questionnaire, and random assignment. Using a crossover design, each participant completed speech recognition testing and sound quality ratings in quiet and noise for all selection methods and a verified NAL-NL2 condition. Results The set of presets can fit 67.9% of older Americans with mild-to-moderate hearing loss (Aim 1). Controlling for hearing thresholds and sound quality ratings, liner mixed-effects models indicated that speech recognition scores for select-by-audiogram, select-by-self-test, and select-by-trying were not statistically different from the NAL-NL2 condition. Statistical analysis indicated that select-by-self-test produced outcomes most consistent with individual outcomes for the NAL-NL2 condition (Aim 2). Conclusion This newly developed fitting paradigm may provide efficacy comparable to best-practice verification and could be used in OTC hearing aids.