Traditional hearing health care (HHC) service delivery models focus on face-to-face, clinic-based testing requiring several patient visits. Globally, access to these services is challenging which results in many individuals living with untreated hearing loss, which has pervasive and far-reaching consequences. With the shift in healthcare towards mHealth and modern machine learning there is potential to make HHC accessible through scalable models of care. This can be achieved through low-cost hearing devices, leveraging smartphone technologies, and equipping a larger number of specialists for medical and surgical management of ear and hearing diseases. Furthermore, computational auditory models, advanced algorithms, and the use of artificial intelligence can positively impact the development of new hearing solutions or the optimization of existing ones.
The goal of this research topic is to provide a comprehensive collection of the current research in hearing sciences. Digitalization, advanced algorithms, machine learning and artificial intelligence are provoking a rapid transformation in healthcare, among other disciplines. This research topic aims to collect the latest research in these areas to pave the way for the effective implementation of digital technologies and computational methods in order to improve accessibility to ear and hearing healthcare services.
Original research articles, systematic reviews, meta-analyses, randomized controlled trials, pilot studies, field studies, observational studies, case reports, and evidence-based perspective articles are welcome in this Research Topic.
Areas to be covered may include, but are not limited to:
? Novel techniques of hearing assessments and rehabilitation inside and outside the hearing clinic
? Optimization of hearing assessments by means of advanced signal processing and machine learning
? Big data and AI in audiology and hearing healthcare
? Computational approaches to improving hearing devices and auditory implants
? Data-based and model-based investigations into hearing loss
? e-Audiology and e-Research (remote hearing healthcare and virtual audiological care and interventions)
? Epidemiological studies making use of large biobanks and bioresources to uncover associations between hearing loss and other conditions
? Use of natural language processing in qualitative and mixed methods for hearing research
? Genetic profiling and deep phenotyping in hearing sciences
? Digital transformation of hearing healthcare and audiological services including teleaudiology
Traditional hearing health care (HHC) service delivery models focus on face-to-face, clinic-based testing requiring several patient visits. Globally, access to these services is challenging which results in many individuals living with untreated hearing loss, which has pervasive and far-reaching consequences. With the shift in healthcare towards mHealth and modern machine learning there is potential to make HHC accessible through scalable models of care. This can be achieved through low-cost hearing devices, leveraging smartphone technologies, and equipping a larger number of specialists for medical and surgical management of ear and hearing diseases. Furthermore, computational auditory models, advanced algorithms, and the use of artificial intelligence can positively impact the development of new hearing solutions or the optimization of existing ones.
The goal of this research topic is to provide a comprehensive collection of the current research in hearing sciences. Digitalization, advanced algorithms, machine learning and artificial intelligence are provoking a rapid transformation in healthcare, among other disciplines. This research topic aims to collect the latest research in these areas to pave the way for the effective implementation of digital technologies and computational methods in order to improve accessibility to ear and hearing healthcare services.
Original research articles, systematic reviews, meta-analyses, randomized controlled trials, pilot studies, field studies, observational studies, case reports, and evidence-based perspective articles are welcome in this Research Topic.
Areas to be covered may include, but are not limited to:
? Novel techniques of hearing assessments and rehabilitation inside and outside the hearing clinic
? Optimization of hearing assessments by means of advanced signal processing and machine learning
? Big data and AI in audiology and hearing healthcare
? Computational approaches to improving hearing devices and auditory implants
? Data-based and model-based investigations into hearing loss
? e-Audiology and e-Research (remote hearing healthcare and virtual audiological care and interventions)
? Epidemiological studies making use of large biobanks and bioresources to uncover associations between hearing loss and other conditions
? Use of natural language processing in qualitative and mixed methods for hearing research
? Genetic profiling and deep phenotyping in hearing sciences
? Digital transformation of hearing healthcare and audiological services including teleaudiology
