Podcast Episode 3: A holistic perspective on hearing technology

n this episode, Brent Edwards from NAL and Stefan Launer from Sonova take us through their careers and share lessons and perspectives on the development of hearing technology. We discuss how the development of technology becomes more holistic, design thinking,  standardization, and what's needed to get to new service models and innovation.

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Twenty-five years of clinical data collection: from a single site relational database towards multi-site interoperability

Over the years Hannover Medical School has build a comprehensive data pool for patients with implantable hearing devices, which serves as a basis for answering various research questions and big data analyses.

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The HiGHmed approach for FAIR use of clinical and research data with openEHR – Focusing on interoperability

Patient-centric medical research benefits from the sharing and integration of complex and diverse data from different sources such as care, clinical research, and novel emerging data types.

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Learning from audiological data collected in the lab and the real world

Research in the last decades with the audiological data led to many important discoveries, and today, as the area of data emerges the focus turns to maturing those discoveries along the dimensions of coverage, applicability, bias, and privacy into solutions that improve the lives for people with hearing problems.

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Read more about the article Big Data and the Apple Hearing Study
Daily LEX8h over time in California, Florida, New York, and Texas.

Big Data and the Apple Hearing Study

The University of Michigan School of Public Health has partnered with Apple Inc. to use advances in smart device and wearable technology to evaluate the levels of sound at which iPhone users listen to music and other media, as well as how long and how often they listen.

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Read more about the article Hearing test using smart speakers: Speech audiometry with Alexa
Illustration of the differences between the clinical test and speech audiometry at home, which can be performed with a smart speaker.

Hearing test using smart speakers: Speech audiometry with Alexa

We present an Alexa skill that performs a speech-in-noise listening test with matrix sentences. The skill is evaluated with four subject groups and in three different acoustic conditions.

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Read more about the article Model-based selection of most informative diagnostic tests and test parameters
The goal is to find the model instance (folder from shelf y) that has the maximum likelihood to have generated the experimental data set d. The stimulus that leads to the smallest variance of parameter estimates is presented next. The process is repeated until the termination criterion is met.

Model-based selection of most informative diagnostic tests and test parameters

The model should conduct the experiment because it knows best which condition is going to be the most informative for confining its free parameters, at least in theory

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Read more about the article Prevalence statistics of hearing loss in adults: Harnessing spatial big data to estimate patterns and trends
Fig.1 Map of England by Government Office Regions, showing prevalence rates of self-reported hearing loss in eight Waves of the English Longitudinal Study of Ageing (ELSA). This work by Dialechti Tsimpida is licensed under a Creative Commons Attribution 4.0 International License.

Prevalence statistics of hearing loss in adults: Harnessing spatial big data to estimate patterns and trends

Harnessing spatial big data to estimate patterns and trends of hearing loss

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Read more about the article A data-driven decision tree for diagnosing somatosensory tinnitus
Overview of the decision tree to diagnose somatosensory tinnitus

A data-driven decision tree for diagnosing somatosensory tinnitus

Based on the results of an online survey, we developed a decision tree to classify somatosensory tinnitus patients with an accuracy of over 80%.

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Examining the association of standard threshold shifts for occupational hearing loss among miners exposed to noise and platinum mine dust at a large-scale platinum mine in South Africa

The association of standard threshold shifts for occupational hearing loss among miners exposed to noise and platinum mine dust at a large-scale platinum mine in South Africa

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Can AI-led hearing healthcare address the growing global burden of hearing loss?

According to the recent WHO World Report on Hearing, there are approximately 500 million people worldwide with disabling hearing loss, the vast majority of whom receive no treatment. The consequences of this unmet need are dire: hearing loss is a top-5 contributor to the global burden of disability; the leading modifiable risk factor for dementia; and costs nearly 1 trillion dollars per year.

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Read more about the article Dynamically Masked Audiograms with Machine Learning Audiometry
Final masked AMLAG results for one participant (127) with a left cochlear implant and no residual hearing. Red diamonds denote unheard tones and blue pluses denote heard tones. The most intense tones at lower frequencies in the left ear were effectively masked.

Dynamically Masked Audiograms with Machine Learning Audiometry

Dynamically masked audiograms achieve accurate true threshold estimates and reduce test time compared to current clinical masking procedures.

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Read more about the article The critical role of computing infrastructure in computational audiology
The nine stages of the machine learning workflow.

The critical role of computing infrastructure in computational audiology

The rise of new digital tools for collecting data on scales never before seen in our field coupled with new modeling techniques from deep learning requires us to think about what computational infrastructure we need in order to fully enjoy the benefits and mitigate associated barriers.

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Speech recognition apps for the hearing impaired and deaf

Speech recognition software has become increasingly sophisticated and accurate due to progress in information technology. This project aims to examine the performance of speech recognition apps and to explore which audiological tests are a representative measure of the ability of these apps to convert speech into text.

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Read more about the article Computational Audiology: new ways to address the global burden of hearing loss
Source: https://www.stripepartners.com/our_writing_article/the-age-of-the-ear/

Computational Audiology: new ways to address the global burden of hearing loss

Computational audiology, the augmentation of traditional hearing health care by digital methods, has potential to dramatically advance audiological precision and efficiency to address the global burden of hearing loss.

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