Harnessing the power of artificial intelligence to transform hearing healthcare and research

Lesica, Nicholas A.; Mehta, Nishchay; Manjaly, Joseph; Deng, Li; Wilson, Blake S.; Zeng, Fan‐Gang

doi:10.1038/s42256-021-00394-z

Cited by 39 publications

(29 citation statements)

References 81 publications

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“…Future advancements in technology, especially in artificial intelligence, may help facilitate the provision of remote care and may accelerate the adoption of tele-audiology services worldwide at affordable costs ( 99 ). To date, artificial intelligence has been implemented in a CI software system (FOX) to allow for autonomous “self-fitting” of the CI device, as well as home-based otoscopy.…”

Section: Discussionmentioning

confidence: 99%

Tele-Audiology: Current State and Future Directions

D'Onofrio

Zeng

2022

Front. Digit. Health

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The importance of tele-audiology has been heightened by the current COVID-19 pandemic. The present article reviews the current state of tele-audiology practice while presenting its limitations and opportunities. Specifically, this review addresses: (1) barriers to hearing healthcare, (2) tele-audiology services, and (3) tele-audiology key issues, challenges, and future directions. Accumulating evidence suggests that tele-audiology is a viable service delivery model, as remote hearing screening, diagnostic testing, intervention, and rehabilitation can each be completed reliably and effectively. The benefits of tele-audiology include improved access to care, increased follow-up rates, and reduced travel time and costs. Still, significant logistical and technical challenges remain from ensuring a secure and robust internet connection to controlling ambient noise and meeting all state and federal licensure and reimbursement regulations. Future research and development, especially advancements in artificial intelligence, will continue to increase tele-audiology acceptance, expand remote care, and ultimately improve patient satisfaction.

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Section: Discussionmentioning

confidence: 99%

Tele-Audiology: Current State and Future Directions

D'Onofrio

Zeng

2022

Front. Digit. Health

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“…The underlying causes of the divergence between human and model invariances demonstrated here seems important to understand, in part because we will not fully understand biological sensory systems until we can replicate them in a model, and deep neural networks are currently the most promising candidates. In addition, many otherwise promising model applications, such as model-based signal enhancement (88, 89), are likely to be hindered by human-discrepant model invariances.…”

Section: Discussionmentioning

confidence: 99%

Model metamers illuminate divergences between biological and artificial neural networks

Feather

Leclerc

Mądry

et al. 2022

Preprint

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Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances we generated “model metamers” – stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from deep model stages, suggesting differences between model and human invariances. Targeted model changes improved human-recognizability of model metamers, but did not eliminate the overall human-model discrepancy. The human-recognizability of a model’s metamers was well predicted by their recognizability by other models, suggesting that models learn idiosyncratic invariances in addition to those required by the task. Metamer recognition dissociated from both traditional brain-based benchmarks and adversarial vulnerability, revealing a distinct failure mode of existing sensory models and providing a complementary benchmark for model assessment.

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“…The utilization of deep learning in CI coding strategy has provided new insights into the 'AI + CI' field. AI not only transforms hearing healthcare and research [8], but also has a great potential for breakthroughs in the present limitations with CI coding strategy. With more advanced network architectures and training methods, deep learning based coding strategies may outperform the present coding strategies.…”

Section: Discussionmentioning

confidence: 99%

“…Deep learning [6], [7], a subset of artificial intelligence (AI) and machine learning, has an increasing influence and potential on the innovation of CI. In the recent decade, AI based approaches have demonstrated their supreme capability over traditional signal processing algorithms in computer vision, natural language processing, gaming, and biomedical engineering, including hearing healthcare [8]. Machine learning and deep learning have changed various aspects of the 'AI + CI' research [9], including prognosis estimation [10], electrode placement [11], robotic surgery [12], mapping [13], and sound signal processing [14], [15].…”

Section: Introductionmentioning

confidence: 99%

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

Huang¹,

Chao²,

Yu³

et al. 2022

Preprint

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<p>ElectrodeNet, a deep-learning based sound coding strategy for the cochlear implant (CI), is proposed in this study. </p> <p>ElectrodeNet emulates the ACE strategy by replacing the conventional envelope detection using various artificial neural networks, and the extended ElectrodeNet-CS strategy further incorporates the channel selection (CS) in the network. Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the Fast Fourier Transformed bins and electrode stimulation patterns from the processing of the ACE strategy for clean speech. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet with the factors of network architecture, dataset language, and noise type using CI simulations. Subjective listening tests for vocoded Mandarin speech were conducted with normal-hearing listeners to measure sentence recognition scores. DNN, CNN, and LSTM based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC) and Spearman’s rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly above average in STOI and sentence recognition compared to ACE. The methods and findings in this study demonstrated the feasibility and potential of using deep learning in CI sound coding strategy.</p>

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Harnessing the power of artificial intelligence to transform hearing healthcare and research

Cited by 39 publications

References 81 publications

Tele-Audiology: Current State and Future Directions

Tele-Audiology: Current State and Future Directions

Model metamers illuminate divergences between biological and artificial neural networks

ElectrodeNet – A Deep Learning Based Sound Coding Strategy for Cochlear Implants

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